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1 qwertyuiopasdfghjklzxcvbnmq wertyuiopasdfghjklzxcvbnmqw ertyuiopasdfghjklzxcvbnmqwer tyuiopasdfghjklzxcvbnmqwerty uiopasdfghjklzxcvbnmqwertyui opasdfghjklzxcvbnmqwertyuiop asdfghjklzxcvbnmqwertyuiopas dfghjklzxcvbnmqwertyuiopasdf ghjklzxcvbnmqwertyuiopasdfgh jklzxcvbnmqwertyuiopasdfghjkl zxcvbnmqwertyuiopasdfghjklzx FacePhi SDK cvbnmqwertyuiopasdfghjklzxcv Software Development Kit bnmqwertyuiopasdfghjklzxcvbn User manual mqwertyuiopasdfghjklzxcvbnm qwertyuiopasdfghjklzxcvbnmq wertyuiopasdfghjklzxcvbnmqw This document is property of FacePhi Biometria S.A. All rights reserved. Total or partial copy of this document is forbidden. This document contains information for internal use; consider it as a working tool.

2 FacePhi 2015 FacePhi Biometria. All rights reserved. FacePhi and F7 Face Recognition Logos are trademarks of FacePhi Biometría S.A. (A ) registered in Spain. Other products and companies here mentioned may be registered trademarks of their owners. FacePhi Biometría often publishes new software versions and upgrades; for this reason the images shown in this document may be different from the ones shown on the screen. Page 1

3 INDEX 1. INTRODUCTION What is facial biometry? Recognition process description Product description FacePhi SDK (Software Development Kit) 6 2. REQUIREMENTS Development Hardware Production Hardware Software Cameras 8 3. INCOMPATIBILITIES 9 4. TECHNOLOGY FUNCTIONING Required previous knowledge Face features extractor module (Extractor) Face pattern Face template Face features Matcher module (Matcher) Matcher security level User structure Face recognition functions Enrolment Authentication Identification Retraining Use characteristics Pose Expression Lighting Glasses Capture devices 20 Page 2

4 4.4. Changes Face Capture device API DESCRIPTION Components Technology settings FacePhi Recognition Matcher Default settings Recommended configuration for 1:1 mode Recommended configuration for 1:N mode INTEGRATION Possible architectures Client-Server architecture (Windows client / Linux server) Client-Server architecture (Android/iOS client + Linux server) Client-Server architecture (Android/iOS client with mobile database + Linux server) DATABASE VERSIONS FREQUENTLY ASKED QUESTIONS (FAQ) General Installation System requirements Licences Functioning CONTACT INFORMATION Commercial contact Technical support Feedback and suggestions 36 Page 3

5 1. Introduction Welcome to FacePhi SDK user manual. In this guide you will find all information needed to know more about FacePhi face recognition software. This manual is divided into two main parts: 1. An introduction which explains the functioning of the facial biometric system, regardless of its implementation, the software requirements or restrictions. 2. Chapters written for developers and integrators, detailing technical aspects of the technology What is facial biometry? Biometry is a science that, through automated methods, is able to verify or recognize the identity of a person based on their physiology or behavioural characteristics. The word biometry comes from the Greek language, bios (life) and metron (measure), which means that all biometrical devices can measure and identify personal features. Each person has morphological features that are unique and therefore, different from everyone else. This technique is based on the recognition of unique corporal features, this is why it recognizes the person for who they are physically and not for what they carry with them (cards, keys, credentials) or for what information they can retain (pin numbers). Several techniques of human verification have emerged in the market recently. They consider different biometric features and it is possible to find software and hardware that supports them. The most used techniques nowadays are the iris recognition, the fingerprint recognition, the voice recognition and the face recognition, which are expected to be used in a massive way in the near future. Thanks to the advances made in multimedia video technology, the identification of facial features has evolved more quickly than expected. This has caused an increase in the number of video cameras at work places or residential complexes, therefore, people are more used to having cameras around and they do not feel like it is an invasion of their privacy. Also, the recognition through facial features is inherent to us all. Specific individuals can be Page 4

6 recognized in a crowd just by looking closely to their faces. Therefore, this type of identification is considered to be the most natural among all biometric systems Recognition process description The facial recognition process allows determining the identity of a person. The identification process is divided into three main phases: detection, extraction and recognition. During the first task, the software locates faces in a photograph or a video. In the second one, it extracts the most representative features in the previously detected face, generating what it is called the facial template. This template is a structure of the facial features extracted from a series of samples or images of the person s face. Finally, in the third task the software compares the extracted template with reference templates which must have been previously stored in a data base, file or any other type of data storage mechanism. The comparison is made analysing the structural elements of the face. Once the system has obtained the necessary features, it compares them with the ones that are already stored in the data base, verifying the identity of the person. Face Detection Processed Face Image (sample) Features Extraction Template Template Matching Verification result Page 5

7 1.3. Product description FacePhi SDK is a product specially designed for developers and integrators who wish to incorporate facial recognition technology into their solutions. It is a development framework which enables integrating this kind of biometrics in a wide range of environments, both ad-hoc and distributed, providing a system capable of operating in real time FacePhi SDK (Software Development Kit) This package is intended for installation on a developing computer and has all the necessary components for the development of solutions based on facial biometry. The content of this package is shown in the following table: Component Content Description Linux development libraries libfphi.matcher.so Native library which includes templates matching functionalities. Java development libraries fphi-licensing-java jar fphi-matcher-java jar Includes necessary classes to handle licensing exceptions of facial recognition modules in Java applications. Includes templates matching functionalities in Java applications. Activation utility lmx-endusertools_linux_x64_rhel5.sh Utility for the activation of FacePhi software licenses. Activation library liblmxvendor.so Library for the activation of FacePhi software licenses. Table 1. FacePhi SDK content 2. Requirements 2.1. Development Hardware Minimum Requirements: Intel Core 2 2,5 GHz 2 GB RAM Page 6

8 100 MB of free disk space Recommended Requirements: Intel Core 3,07GHz 4 GB RAM or higher. 100 MB of free disk space Production Hardware Minimum requirements: Intel Core 2 2.5GHz 1 GB RAM Recommended requirements: Intel Core 3.07 GHz 2 GB RAM However, information hereby is merely indicative due to the fact that hardware requirements for a production machine depend on the characteristics of the final application developed from FacePhi SDK To have a more precise idea of the hardware requirements for the production machines it is recommendable to read the performance statistics in the technical specifications document Software Operating Systems: Red Hat Enterprise Linux 5 (or higher) Requirements for development: JDK 1.7 (Java) Glibc 2.5 (C++) Page 7

9 2.4. Cameras In this section you can find some recommendations or general guides for the cameras characteristics. However, the election of the type of camera as well as the characteristics of it, are matters that will be directly constrained by the environment in which the final solution will be implemented. Therefore, the type of camera and its characteristics will not be the same in an external or internal environment. Conditions for an external environment (lightning, climate, etc.) will change substantially. As it is well known, cameras are not the only source to get input images from. However, if you wish to develop a face recognition application that works with images captured from a camera, it is required that these cameras meet some specifications in order for the images to have enough quality to be analysed. Camera minimum requirements: 640 x 480 resolution 24 fps or higher Recommended requirements: 640x480 resolution 30 fps or higher Autofocus Automatic Iris Wide Dynamic Range Technology Page 8

10 3. Incompatibilities FacePhi SDK is not compatible with previous versions to version 5.0. Installing SDK version in a development device along with another version previous to 5.0 is incompatible. Facial templates generated with previous versions to 5.0 are not compatible with the recognition module provided by SDK version Technology functioning This section is aimed to explain the groundwork and functioning of FacePhi face recognition technology. Also, it is important to establish the required steps to get an optimum performance of the technology. Face recognition can be performed in two different ways: identification or authentication. Using the identification mode, the recognition system identifies people without knowing their identity beforehand. On the contrary, authentication means that the system verifies the identity of a person having a previous knowledge of that persons identity (it checks that the person really is who he claims to be). For authenticating or identifying a person it is necessary to have a previous registry of the facial features of the group of people you wish to work with. This is why; prior to any recognition process you must register the facial features of those individuals who will be part of the system or application (individuals domain) Required previous knowledge Face features extractor module (Extractor) This module analyses images with the goal of synthesizing the information of the faces appearing in an image. For this purpose, the extractor will first detect the faces in the image to then analyse whether they meet certain quality levels, size, pose, etc. The system extracts the face pattern to the faces which meet these requirements, the ones which don t, are rejected. Page 9

11 Face pattern A face pattern is a mathematical representation of a person s face characteristics. The extractor module is in charge of creating the face pattern and this is done based on just one image of the person s face which meets some quality requirements (See section Use characteristics ). The next image shows an example of how the system obtains a face pattern from a face with an expression of happiness. Input Image Face features extraction (Extractor Module) Representative face pattern of the person's face in the image Face template A face template is a set of face patterns which represents one individual. In order for this face template to be valid for future recognition processes, all the patterns shaping that template must belong to the same person. Page 10

12 Input Images Face features extraction (Extractor Module) Face template with representative patterns of the person Face features Matcher module (Matcher) This module performs the recognition process. It works in two different ways; deciding if a person really is who he/she claims to be or finding out the person's identity. The matcher compares face features obtained from the extractor module and decides, according to a security level, if they belong to the same person Matcher security level The matcher security level is a configurable parameter which indicates the similarity values from which the system will consider if two different patterns belong to the same person or not. Take the example of a "high" security level and a "medium" one, which represent a similarity between patterns of 90% and 80% respectively. If we compare two patterns with a security level set up in "medium" and the result is a similarity of 85%, then the system will assume those patterns belong to the same person. However, if the security level was set up in "high", the result of the similarity does not overcome 90% and therefore the system would consider those patterns to belong to different people. The security level must be set up according to the security requirements of the application using face recognition. Page 11

13 User structure The user structure contains all the essential and unique face characteristics, for classifying and distinguishing an individual from the rest. This structure is created from a face template; therefore, it is highly recommendable to ensure the variability and different face expressions among the patterns which configure the face template. When creating the user structure, the system will delete the most redundant and similar patterns, those that do not provide enough differentiation. In the following example, we can see how a user structure is created out of information from a user with three different face expressions (happiness, sadness and anger). Representative face template of the user Creation of user structure (matcher) User structure Page 12

14 4.2. Face recognition functions Enrolment In order for a person to use the face recognition process, it is necessary to previously register the person by enrolling his/her face characteristics. This way, the system has enough information to compare with and decide whether to give a positive match or not. The process of registering an individual is the following: 1. Generate a representative face template of the person with the extracted face patterns from images of their face. 2. Generate a user structure from the template. 3. Store the user structure in order to use it in future recognition processes. Process is shown below: Input images Face Features Extraction Representative face template of the user Creation of user structure User Structure DB Storage of user structure It is recommendable that the registry is done from a face template configured with the highest possible number of patterns from the users face. This way, the system will store information from different poses and face gestures, facilitating a positive match. On the contrary, if the registry is done from a template with just one pattern, the probability of Page 13

15 success in future recognitions will decrease. In this case the user will only be recognized if the face gesture and pose is very similar to the one used in the registration Authentication Authentication mode, also known as 1:1 mode, verifies the identity of a person. For this, the system needs to compare between two entities: A representative pattern template of the person to authenticate A representative user structure of that person (stored in the database) Verification will be successful if similarity obtained from the comparison is higher than the security level established on the matcher. The process for verifying a persons identity is the following: 1. Generate a representative face template of the person with the extracted face patterns from images of their face. 2. Obtain the user structure corresponding to that person from the database (stored during registration process). 3. Compare both entities using the matcher. Page 14

16 Process is shown below: Comparison will be successful if the established security level is overcome Comparison (matcher) Input images Extraction of facial features Representative Face Template of the user DB Comparison Result Database Obtain user structure of that person User Structure Identification Identification mode, also known as 1:N mode, finds out the identity of a person. It consists of an iterative verification between the face template of the user you wish to identify and all the user structures (stored in a database). Necessary steps to identify a person: Page 15

17 1. Generate a representative face template of the person with the extracted face patterns from images of their face. 2. Obtain all user structures stored in the database (representative of all the users enrolled in the system). 3. Perform consecutive verifications between the face template extracted and all the user structures stored, using the matcher module. Identification will be successful if, after verifying the face template against all of the other user structures, one of the comparison results is positive and overcomes the security level set up. Page 16

18 This process is shown below: Comparison will be successful if the established security level is overcome Comparison (matcher) Input Images Extraction of facial features Representative Face Template of the user DB Database Obtain user structures Registered user structures Identification Result For both authentication and identification, the number of patterns forming the face template you wish to recognize, will depend on the type of application for which it will be used. If the template has a higher, more variable number of patterns, the result of the comparison will be more accurate. However, a template with a higher number of patterns can mean, depending on the application, a less friendly system. For example, in an access control, a template with a higher number of patterns means the person will have to stand in front of the camera for longer in order for the system to take a sufficient number of frames. Page 17

19 Retraining Retraining user structures allows improving the face recognition process when aspects such as time, variability in the face expression or lightning changes in the image, are taken into account. As mentioned before, a user structure differentiates a person from the rest but only under a subset of conditions during the registry (light, facial expression, etc.). If these conditions vary from one comparison to another, recognition of the user will be slightly diminished. For these cases, we can count on the retraining function, which will feed the user structure with new representative patterns on new conditions. Please find on the following image an example applied to facial expression variability. The user on this example was registered with a template formed with face patterns representing a happy, sad and an angry face. Then, the same user proceeds to authenticate using a template with just one pattern with a surprise expression. The comparison between the input template and the user structure may give a result of low similarity, forcing the system to repeat the authentication process several times until the user has an expression which resembles enough to one of the expressions in the user structure. To avoid this from happening, once the person is recognized, the user structure can be retrained with the new template. This way, in future recognition processes this person will be recognized more easily when having a surprise expression. Page 18

20 Add pattern from face template to the user structure Retraining (matcher) Representative user template User structure retrained User Structure This process is also applicable when there are lightning variations. If a person registered using images with an even light and then, recognized under bad lightning conditions, like backlight for example, retraining the user structure with new patterns will ensure a substantial improvement in these new conditions. However, using the retraining function under weak security conditions can cause some inconveniences. To retrain a user structure, first it must be ensured that the new pattern or face template which will retrain it must really belong to the user (for example, user A ). If this is not the case, this could cause a security error, because the user A structure can be retrained with information from a different user (for example, user B ). As a consequence of this error, user B could be recognized as user A. Page 19

21 4.3. Use characteristics Face recognition is very sensitive to the quality of the images. For this reason, we should pay special attention when we acquire them Pose Front position is the best face position (full-face). Head movements should not exceed 15 degrees on each direction. These movements are the following: Nodding up and down (Pitch) Turning right or left (Yaw) Tilting left or right (Roll) Expression FacePhi face recognition technology is able to obtain, enrol and verify face patterns even when there are face slight movements and different face expressions. Nevertheless, if there are important changes on the face expression, the software may require a more natural position Lighting Lighting is a very important factor. The light should be as diffuse as possible in order to be equally distributed all over the face (a diffuse light is the opposite of a hard or directional light which causes shadows and highlights). Visible shadows should be avoided. Shadows might appear when a high intensity light is used or when there is backlighting on the scene. For this reason, we advise you to avoid such situations Glasses Situations where the lighting may cause reflections on the crystals of the glasses should be avoided. If necessary, remove the glasses during the extraction process. Recommended glasses are those ones with clear and transparent crystals because, both eyes and iris are perfectly visible. Sunglasses are not accepted due to their dark lenses which cause eyes occlusion Capture devices Recommended resolution: 640x480 (VGA). Webcams are the most common capture devices. Page 20

22 Ideally, all images should be registered and identified using the same webcam. Different capture devices might have different optical distortions that might affect the functioning of the face recognition system Changes Face Beard, moustache and some other facial characteristics may affect the reliability of the recognition process but, however, the system has a high level of tolerance against these changes Capture device As mentioned before, the lens system is very important. When the capture device changes, generally, the lens system also changes and so do the perception (each lens system can deform the image in a different way). Besides, each device counts on a different sensor which encodes the information and it can also introduce differences. Sometimes, when the camera is changed, the system may require a new registration. Page 21

23 5. API Description This section describes the contents of the API. For more detailed information see the help files Components FacePhi SDK is a set of C++ and Java development libraries which allows you to use features included in the matching module. Moreover, the system offers additional modules that complete the product, like the software activation libraries and the utility used to install the license server. For further information about the different APIs included, please review these documents: - FPhi SDK C++ Matcher API.pdf. - FPhi SDK Java Matcher API.pdf Technology settings FacePhi Recognition Matcher Security Level (MatchingSecurityLevel): determines the recognition process reliability and therefore, the security level of the system. Values supported for this parameter from lowest to highest are as follows: o MediumSecurityLevel o MediumHighSecurityLevel o HighSecurityLevel o VeryHighSecurityLevel o HighestSecurityLevel Comparison reliability offered (on) by a user structure (TemplateReliability): Represents the reliability offered by a user structure during the comparison process. This value is closely related to the number of face patterns stored in the user structure. Also, this value is taken into account when creating the user structure for the pattern packaging. The higher the value, higher the number of patterns packaged in the user structure and therefore, a greater number of comparisons between patterns when comparing templates. This increase in the number of comparisons translates into a higher reliability on identification and verification processes. Values supported are: Page 22

24 o MediumTemplateReliability o HighTemplateReliability o VeryHighTemplateReliability o ExtremeTemplateReliability MediumTemplateReliability represents medium reliability (lower number of stored patterns) and ExtremeTemplateReliability represents maximum accuracy (higher number of stored patterns) Default settings The default system configuration is shown in the following table: Parameter Range Value Security Level (MatchingSecurityLevel) User structure reliability on comparison (TemplateReliability) MediumSecurityLevel MediumHighSecurityLevel HighSecurityLevel VeryHighSecurityLevel HighestSecurityLevel MediumTemplateReliability HighTemplateReliability VeryHighTemplateReliability ExtremeTemplateReliability HighSecurityLevel HighTemplateReliability Table 2. Default settings Recommended configuration for 1:1 mode Recommended configuration for the recognition system on facial verification mode 1:1 (user authentication), is shown in the following table. Parameter Range Value Security Level (MatchingSecurityLevel) User structure reliability on comparison (TemplateReliability) MediumSecurityLevel MediumHighSecurityLevel HighSecurityLevel VeryHighSecurityLevel HighestSecurityLevel MediumTemplateReliability HighTemplateReliability VeryHighTemplateReliability ExtremeTemplateReliability HighSecurityLevel HighTemplateReliability Page 23

25 Table 3. Recommended settings for 1:1 mode Recommended configuration for 1:N mode Recommended configuration for the recognition system on facial identification mode 1:N (user identification) is shown in the following table. Parameter Range Value Security Level (MatchingSecurityLevel) MediumSecurityLevel MediumHighSecurityLevel HighSecurityLevel VeryHighSecurityLevel HighestSecurityLevel HighSecurityLevel User structure reliability on comparison (TemplateReliability) MediumTemplateReliability HighTemplateReliability VeryHighTemplateReliability ExtremeTemplateReliability HighTemplateReliability Table 4. Recommended settings for 1:N mode Important: all previously recommended configurations were established supposing that the same camera is being used at all times, for both the registration and verification processes. Nevertheless, even if the camera is changed, the system can be used with this same configuration. However, there cannot be serious alterations in the camera conditions or lighting. If these conditions drastically change, it is possible that the system is not able to recognize the users correctly, generating what is known as false rejections. If this were to happen, it will be necessary to reset the system conditions or register once again the users that were affected by these changes. Page 24

26 6. Integration FacePhi SDK is a system designed to be fully scalable and adaptable to a variety of environments. Here there are some examples of possible architectures that can be implemented through the product Possible architectures Client-Server architecture (Windows client / Linux server) Another option is implementing a client-server system, for example, by using a web service: Network (LAN, Internet ) Graphics User Interface (GUI) Web Service SOAP / Web Service REST Windows Forms Application WPF Application FacePhi Extractor FacePhi Matcher Data Access Layer DB Z Figure 1. Client-Server architecture In this architecture, the client s application would be in charge of extracting the facial templates and then, send them to a server. The server would be responsible for managing the registration and comparison of facial templates. Page 25

27 Another option is to implement a java web application which hosts a web control: Network (LAN, Internet ) Web Browser Java Web Application FacePhi Extractor Webv FacePhi Matcher Data Access Layer DB Figure 2. Client-server architecture through web browser In this architecture, the Silverlight control is in charge of extracting the patterns of the client s side. The registration and verification of patterns will be carried out on the server. Page 26

28 6.1.2 Client-Server architecture (Android/iOS client + Linux server) With this structure it would be possible to build a client-server model. The client is an Android or ios application which integrates the facial features extraction module. By using a web service, it is communicated with a server where just the recognition module is hosted. Graphic User Interface (GUI) Android / ios Application Others FacePhi Extractor Network (LAN, Internet ) Web Service SOAP / Web Service REST FacePhi Matcher Data Access Layer DB Figure 3. Client-Server architecture (local database) Page 27

29 6.1.3 Client-Server architecture (Android/iOS client with mobile database + Linux server) With this structure it would be possible to build a client-server model. The client is an Android or ios application which integrates the facial features extraction module and users storage. By using a web service, it is communicated with a server where just the recognition module is hosted. Graphic User Interface (GUI) Android / ios Application Others Data Access Layer FacePhi Extractor DB Network (LAN, Internet ) Web Service SOAP / Web Service REST FacePhi Matcher Figure 4. Client-server architecture (local database) In this architecture, the client s application would be in charge of extracting the patterns in order to send them afterwards to the server. The server would be in charge of the face Page 28

30 patterns comparison in order to return all data needed for registration as well as the response for authentication and identification. 7. Database As mentioned before, FacePhi SDK does not implement any data access logic for the storage of templates. Instead, it provides the necessary mechanisms for the extraction and recognition of templates. Nevertheless, a scheme for the storage of facial information of users should include at least the following data: ID user identification DATA facial data of the user. MySQL SQLServer Users ID : varchar DATA : longblob Users ID : varchar DATA : varbinary(max) PostgreSQL Oracle Users ID : varchar DATA : byte Users ID : varchar DATA : blob Figure 5. Necessary data to implement a solution based on FacePhi SDK Page 29

31 8. Versions SDK First SDK version available for Red Hat Enterprise Linux environments. 9. Frequently asked questions (FAQ) For any inquiries or malfunctioning, please contact FacePhi s technical department (see section Contact information ). Write down the error code and message if that is the case and try to explain the problem in detail General What is facial biometry? Biometry is a science capable, through automated methods, of identifying and verifying the identity of a person based on physical or behavioural characteristics, in this case, facial characteristics. Why not use another type of biometry? Facial recognition is less intrusive than other types of biometry. Also, it does not require a high level of collaboration from the user and identity theft is practically impossible. What is FacePhi SDK? FacePhi SDK is a development API designed for integrators which provides all of the necessary methods and structures for developing recognition solutions based on Red Hat Enterprise Linux environments. Where can I get documentation for FacePhi SDK? All the necessary documentation for FacePhi SDK is provided in the download server of FacePhi. Where can I get FacePhi SDK? Please contact our commercial department through the contact information provided in section Contact information. Where can I get support for FacePhi SDK? Page 30

32 If you have any technical consultation, suggestion or report, please contact our technical support department. Contact information can be found in section Contact information. Is the user image stored? No. FacePhi SDK does not work with images directly, but with a representation of the most important features of the image. Therefore, it is not possible to reconstruct an image of the user s face. Is there a problem if I wear glasses? No, as long as the minimum requirements are reached (see section Use characteristics Glasses ). Does the system work if I change my appearance; haircut, beard, etc.? Yes. The system should keep functioning as normal, as long as the restrictions are met and there are no substantial changes in the face (hair covering the eyes, etc.) (See section Use characteristics ). Do I need to collaborate in any way in order for the system to work? No. The main recommendation is having a natural pose while looking at the camera (see section Use characteristics ). Can a family member access the system for me, for example, a twin brother? No. The core of our system FacePhi SDK assures discrimination of impostors. Several tests have been made with twins and the system has never incurred in an error Installation How do I install FacePhi SDK in my system? FacePhi SDK is distributed with a simple installer which will guide the user during the product installation. In how many machines can I install FacePhi SDK? It depends on the type of license you acquired. Usually FacePhi SDK is licensed for one device. This type of licensing validates the software installation in only one machine. If you wish to have more information regarding this matter please contact our commercial department through the contact information you can find in section Contact Information. Page 31

33 9.3. System requirements What kind of hardware do I need for using FacePhi SDK? See section Requirements Development Hardware y Requirements Production Hardware. Do I need to have any plug-ins installed in order for FacePhi SDK to work? See section Requirements - Software. What platform does FacePhi SDK work on? See section Requirements - Software. What type of camera do I need to use with FacePhi SDK? As it is known, cameras are not the only source to get images to work with. However, in case you wish to develop an application using face recognition which works with images obtained from a camera, it is necessary that they meet minimum requirements in order to get images with sufficient quality to be analysed. Minimum camera requirements: 640 x 480 resolution 24 fps or higher Recommended: 640x480 resolution 30 fps or higher Autofocus Automatic Iris Wide Dynamic Range Technology Page 32

34 9.4. Licences What do I need to do in order to get a license for FacePhi SDK? Reach our commercial department through the contact information you can find in section Contact information. How do I activate a license for FacePhi SDK? In the product installation package you can find a utility to activate the FacePhi software. It allows activating and installing licenses for the supplied serial numbers. For more information regarding license activation consult the document FacePhi LM Software Activation Guide. What type of license can I get for FacePhi SDK? Currently FacePhi SDK is licensed individually for each machine. These licenses will depend on the type of application you wish to develop:.net, Java or native desktop applications, web applications using our Silverlight user control, Android or ios applications and.net applications integrated into the Windows login. If you are interested in getting more information about this matter or in acquiring another type of license, please contact our commercial department through the contact information in section Contact information Functioning Under which lighting conditions should FacePhi SDK work? Lighting is a very important factor. If possible, light should be equally distributed over the face. Visible shadows on the face should be avoided. This will occur especially when direct lighting is used or when there is backlighting in the captured scene. It is recommended avoiding these types of situations. What is the required distance between the user and the camera to be recognized by FacePhi SDK? The required distance can be adjusted. The maximum distance is determined by the parameter minimumdistancebetweeneyes which can be adjusted depending on the image resolution or final needs. The minimum distance is determined by the parameter maximumdistancebetweeneyes (See section API Description - Technology Settings ). Page 33

35 In an image with a resolution of 640x480 pixels and a minimum distance between eyes of 70 pixels, the maximum distance from the camera is around 75 centimetres. Note that this may vary depending on the optical characteristics of the capture device (wide lens, zoom, etc.). How many faces can the system detect in one image? The system can be adjusted to work either in multiface or single-face mode. In the multiface mode, the system tries to detect all the faces in one image. In single-face mode, from all the detected faces, the system will work only with the largest size face (the one that appears closer). Will FacePhi SDK recognize me if I am wearing glasses? Yes, but it depends on several factors. Situations where lighting causes reflects on the lenses should be avoided. If reflects could not be avoided, it is best if the glasses are removed for the capture process. It is recommended that only clear, see-through glasses are used so that the eyes and iris are clearly visible. Sunglasses are not accepted for their dark lenses, causing occlusion of the eyes. How do changes in the face from ageing affect the recognition process of FacePhi SDK? FacePhi SDK tolerates moderate face changes due to ageing. However, if more radical changes occur, the system might not be able to correctly identify the user, having to enrol the user once again. What position should the user have to be recognized by FacePhi SDK? Optimal position of the face is frontal or full face. Head movements should not overpass 15º degrees on each direction. These movements are the following: Nodding up and down. Turning right or left. Tilting right or left. Page 34

36 10. Contact Information For general inquiries, please, contact us through the following: Website Headquarters México Avenue, 20 Alicante, Spain Telephone (+34) Commercial contact For any commercial inquiry, please, contact us: Telephone (+34) Technical support For any technical question, suggestion or report, please, contact us: Telephone (+34) Page 35

37 10.3. Feedback and suggestions If you would like to make any suggestion or if you detect any type of error, please, contact us: Telephone (+34) Page 36

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