Face Image Data Acquisition and Database Creation

Transcription

1 Chapter 3 Face Image Data Acquisition and Database Creation 3.1 Introduction The availability of a database that contains an appropriate number of representative samples is a crucial part of any pattern recognition research. Face image data acquisition and creation of database have been of great interest to computer scientists and engineers for the last few decades. The accuracy of results of face recognition research highly depends upon the versatility (presence of moderately large representative samples) of the database used [Delac et al. 2008], [Tolba et al. 2006], [Li & Jain 2004]. As face recognition continues to be one of the most popular research areas of computer vision and machine learning, a number of face databases have been created by many researchers. However, many of these databases are tailored to the specific needs of the algorithm under development. FERET, NIST MID, AT & T (formerly ORL), UMIST and Yale are some of the important publicly available face databases [Phillips et al. 2003], [Bolme et al. 2003], [Blackburn et al. 2001], [Phillips et al. 2000], [Belhumeur. et al. 1997], [Rowley et al. 1998], [Sung & Poggio 1997]. Facial Recognition Technology (FERET) database consists of 1199 subjects 39

2 with two facial expressions, two illumination conditions and 9-20 pose variations. It was collected at George Mason University and the US Army Research Laboratory facilities as the part of FERET Programme, sponsored by the US Department of Defense Counter Drug Technology Development Programme [Phillips et al. 2003], [Phillips et al. 1998]. There are total images in the FERET database. NIST Mugshot Identification Database (MID) consists of 1573 subjects having frontal and profile view of varying resolution. There are total of 3248 images in the NIST MID where males outnumber in count [Li & Jain 2004]. AT & T (formerly ORL) database was collected between 1992 and 1994 [Samaria & Harter 1994]. UMIST and YALE Face databases are the other two databases which are commonly seen used in the literatures. They contain a total of 564 and 165 images respectively. Even though all these databases are reported to be publicly available there are some barriers like, distance, bandwidth problem etc., to make the database available for research. Therefore, our attempt is to make available a moderately large database for face recognition research. This chapter presents the work carried out to create a moderately large and representative database of human face images to this effect. In this thesis the standard AT&T(formerly ORL) face database is used as a benchmark for comparative study of face recognition results carried out in the present work with that reported in the literature. For the purpose of an exhaustive study a relatively larger database created by author, named Kannur University Face Database (KNUFDB) is used. Section 3.2 describes AT&T face database used and section 3.3 present the creation of KNUFDB in detail. 40

3 3.2 AT & T face database 3. Face Image Data Acquisition and Database Creation A standard face database known as AT & T (formerly ORL database) has been used for the present study apart from the one we developed. AT & T database contains face images of 40 distinct persons. Each person has ten different images, taken at different times, totaling to 400. Figure 3.1 shows the 40 individuals in the AT & T Face database. Each face image in the database has a size 112x92 pixels. There are variations in facial expressions such as open/closed eyes, smiling/ non-smiling, and facial details such as glasses/no glasses etc. All the images were taken against a dark homogeneous background with the subjects in an up-right, frontal position, with tolerance for some side movements. There are also some variations in scale. Though the database has been used in many face recognition researches, it is clear that the number of samples or database size is too small to prove and establish/reason the eventual results. A database with higher size is essential for proving the correctness or accuracy of the the face recognition researches. Therefore, a larger database has been developed for this research. The succeeding sections gives the details of this face database, that has been developed for the present research. 3.3 KNUFDB face database Collecting a large amount of sample patterns is as important as developing recognition methods for pattern recognition applications. In the present work sufficient attention is given to collect large set of face patterns. The samples were collected with minimum constraints so that it reflects real life environment, and can be used to train and evaluate face recognition systems. The 41

4 Figure 3.1: Face images in AT & T face database - frontal profiles 42

5 images in the database cover a wide set of pose and illumination variations. The images were recorded with a 3mm digital mobile camera, subsequently processed, and then converted to 8bit gray scale images. The resulting images are stored in 150 x 120 pixels size. The subjects were not imposed on pose or expression constraints. The resulting changes in facial expression are typically subtle, often switching between neutral and smiling. The images were recorded by asking the subjects to rotate their head and body. The pose angle varies from +80 o to 80 o. Ground-truth information including gender, age group, session, if the subject is wearing glasses, indoor/outdoor are also provided for each image in the database. The entire database is collected from 110 individuals (includes 77 male subjects and 33 female subjects). The subjects belong to 12 to 60 age groups. The average age of male subjects is 32 years and that of female is 23 years. The captured images in the mobile are transferred to the computer using USB connector and the collected face images are cropped to a 112 x 92 pixel size. In order to make sure that the prepared sample data are perfect, all images of faces have been manually checked and necessary corrections were made using the image editor, Adobe Photoshop. Sample face images (112 x 92) are stored in the database in grayscale Portable Gray Map (PGM) file format with one byte per pixel. This format will definitely help the researchers to experiment with various preprocessing techniques including smoothing, filtering, thresholding, edge detection or feature extraction methods in grayscale domain. The developed face image database is called KNUFDB (Kannur University Face Database). Figure 3.2 shows frontal profile of all 110 subjects in the KNUFDB database and figures show examples of other profiles in the KNUFDB face database for typical persons. 43

6 Face images in KNUFDB face database - contd. in next page 44

7 Figure 3.2: Face images in KNUFDB face database 45

8 Figure 3.3: Sample face images in the KNUFDB face database shows all the instances of a person in the database. 46

9 Figure 3.4: Sample face images in the KNUFDB face database shows all the instances of a person in the database. 47

10 Figure 3.5: Sample face images in the KNUFDB face database shows all the instances of a person in the database. A unique nomenclature is used for labeling each face image sample in the database. This unique labeling system will allow individual researchers to 48

11 compare the performance of different algorithms on specific face image samples. The nomenclature we adopted is XGIIIISSS where X: Gender of the subject, e.g. M or F G: Age Group of the subject, A for Age group 0-10, B for 11-20, C for 21-30, D for E for 41-50, F for 51-60, Z for 61. IIII : Subject Identification Number, e.g SSS : Sample Number from same subject, e.g. 015 The database developed contains 6600 facial images comprising of 110 different individuals with 60 samples for each person. There are various changes in the individual samples of the images collected, including pose variation, expression changes, varying illumination, with specs and without specs, zoom in and zoom out effect. Figure 3.6 shows examples of aspects like, pose variation, illumination changes and varyings expressions of typical persons in KNUFDB face database. 3.4 Conclusion An attempt is made to collect moderately large and representative samples of facial images. A well structured and standard database of human face images, which facilitates research in human face recognition, is developed. The key features of the database are All image samples are stored in PGM file format providing maximum possible information in gray scale. As the data were collected under minimum constraints it incorporates maximum variability. 49

12 Figure 3.6: Expression, database. Pose and Illumination variations in KNUFDB face 50

13 It has balanced representative data among age, sex and sessions of the subject. Database is divided explicitly into training and testing sets to facilitate sharing of results among research community all over the world. The constructed database has the potential to be used as a benchmarking resource for the face recognition researches. All the images are acquired using mobile phone camera so that it can be used for studying the challenges in such images. Also, it helps to design authentication or recognition systems based on mobile devices i.e., handhold recognition/verification devices. Hence the database developed in the present work will certainly help researchers to develop and validate various recognition schemes for human face recognition. 51