Peg-Free Hand Geometry Verification System Pavan K Rudravaram Venu Govindaraju Center for Unified Biometrics and Sensors (CUBS), University at Buffalo,New York,USA. {pkr, govind} @cedar.buffalo.edu http://www.cubs.buffalo.edu Abstract. Biometric authentication systems are gaining importance in this recent world prone to security threats in every field. Hand geometry verification systems use geometric measurements of hand for verification of individuals. It is believed that the combination of different features of the hand is unique for a particular person. Different hand geometry authentication systems use reference pegs for capturing the image of the hand. We propose a peg free hand geometry image acquisition and feature based verification system. Significant geometric features of the hand are extracted from the image. The recognition process is involves matching various weighted features with pre-stored templates. We have built our own image acquisition system and carried out experiments with a medium range data set of 250 images. 1 Introduction Biometric authentication systems find application in various secured access control systems, identification in crime investigations and in many more critical applications dealing with national security. Until recently the most widely used authentication systems are the finger print based authentication and verification systems. Other forms of authentication and verification systems include iris, hand-geometry, face, voice, etc. As the necessity for verification is increasing in every field, for some kinds of access control like office entrance, repository entrance where the requirement is verification, invasive biometrics (eg., fingerprints, iris) may not be desirable as they infringe on privacy. So hand geometry is gaining importance for verification while fingerprints and iris are used for identification. The main idea of biometrics is to differentiate various biological and behavioral features that are considered unique to a person. Different biometric techniques are used for identification and verification of the persons. Each of these techniques has their own advantages and disadvantages. For example Finger print and iris verification systems are considered the best biometrics in terms of FAR and FRR but on the other side they are not cost effective for some implementations like simple access controls. While on the other hand, speech recognition based authentication systems prove to be cost effective for simple access control implementations but cannot be used in high security zones. Trade-off between these two is the hand-geometry based authentication system that is cost effective and can be used in low to medium security zone. The major advantage of the hand geometry verification system is ease of image acquisition compared to fingerprint and iris image acquisition. The system just requires properly placed camera that can get the image of the hand. With these advan-
tages, combining other forms of biometrics like fingerprint with hand-geometry improves the confidence levels in identification procedures. Human hand can be categorized by the length and width of the fingers, geometrical differences in the shape of the hand, and also using special features like lack of one or more fingers or presence of excess fingers. Traditional pegs based image acquisition systems fix the hand placement on the system using the pegs. They capture the image of the hand along with the pegs and extract the features of the hand from that image. Pegs cause deformation in the hand, which often reduces the accuracy of the features extracted. Also it will be difficult for people with deformed hands (who represent 2% of the total world population) to use the verification system. It also poses problem to verification systems deployed in schools where small children find it difficult to place their hand in between the pegs. In this paper we propose peg-free, orientation-free hand geometry verification system, which is inexpensive to implement yet has good verification capabilities. The main idea behind this system is to extract and compare various features of the hand that form a unique set. While comparison, weights are assigned to various features depending on their uniqueness. The procedure of the verification system involves three basic steps: 1. Image Acquisition 2. Feature Extraction 3. Matching. The various steps and algorithms involved in these three basic steps are discussed in detail in the following sections. 2 Previous work Jain[1] developed a prototype for hand geometry based verification system for web security system. They tested on image set consisting of around 360 images and obtained a false acceptance rate (FAR) of 2% and a false rejection rate (FRR) of 15%. Jain and Duta[4] developed a verification system based on alignment of finger contours and measured the mean alignment error between them. They experimented with 353 images from 50 persons and report FAR of 2% and FRR of 3.5%_ Raul Sanchez Reillo[7] implemented hand geometry verification system. They used a database of 200 hand images from 20 people and report 97% success in identification and error rates below 10% in verification. Oden, Ercil and Buke[6] reported a system for identification and verification using implicit polynomials. They combine their method with geometric features and achieve 95% success in identification and 99% success in verification. The training set they used consists of 40 images. 3 Image Acquisition: The image acquisition system that we have designed consists of a camera placed at a certain height from the flat bed on which the user places the hand. The flat bed is a translucent flat base under which light source is placed to avoid shadows caused by
lights in the environment and also to clearly distinguish between the hand and the background. The hand is placed in the range of the camera, which takes the image of the hand. We have considered only the top view image of the hand for feature extraction, the reason being, the two dimensional width of the hand varies as the person gains or looses weight, but the finger lengths and widths are characterized by the bone structure that are time-invariant, after your growth period. During the enrollment phase, 3 hand images of the user are collected. Feature vectors are extracted from these three images and stored in separate files for matching. The user is asked to stretch his hand differently each time the image is acquired to avoid differences arising due to stretching of his hand. The camera is plugged onto a computer that gives a visual feedback of the acquisition process. The camera captures 640*480 resolution colored images of the hand. The images acquired by our set-up are shown in figure [2]: Fig[1].Prototype hand geometry scanner. Fig[2] Images of the hand
4. Feature Extraction: The hand-geometry verification system uses the features of the hand for authenticating the user. The features of the hand include the width and height of the fingers. The colored image obtained from the scanner is processed before the features are extracted. The colored image is first converted to gray scale image using equation (1) and then converted to binary image based on a binary threshold BT which is the average pixel value of the total image. Gray pixel G = 0.3RED + 0.59GREEN + 0.11BLUE. (1) The image thus obtained consists of noise as shown below in figure[3]. Next step involves extraction of single pixel width contour based on binary transition. Using algorithm similar to the chain code contour extraction method [8], the longest contour representing the contour of the hand is extracted. The rest of the contours are eliminated considering them as noise in the image. Fig. [3]. 1. Gray scaled image 2. Binary image 3. Contour (with noise) 4. Contour (noise eliminated). The valley ends in between the fingers and tip ends at the top of the fingers serve as the landmark points for the extraction of features. The lengths and widths of the fingers are calculated using these end points. To obtain these end points, first the curves of the finger ends are extracted. For every point p on the hand contour, the angle made by the lines joining the p and offset points is calculated as shown in figure[4] using the cosine rule given in equation.2.
2 2 2 ( b + c a ) Angle θ = ( 2bc) (2) Where a is the distance between the two offset points, b is the distance between the point p and the offset point before p and c is the distance between p and offset point after p. Fig. [4]. Angle calculation for curve points. If θ is less that threshold angle (θ th ) then the point p is considered as curve point cp. Curve points of all the fingers are separated and are used to find the tip and valley ends. The perpendicular bisector of the line joining initial and final curve points (shown in figure [5] ) of a finger is calculated using equation (4) and the minimum perpendicular distance from curve points of that finger to the perpendicular bisector is calculated using equation (5). The points that yield minimum distance to the perpendicular bisector are considered as the tip or the valley ends of the finger depending of the curve points under consideration. Slope M = ( x2 x1) ( y2 y1). (3) ( y1 y2) ( x1 x2) + y 2 + = M x 2. (4) Where (x1,y1) and (x2,y2) are the coordinates of the initial and final curve points. M is the slope between the offset points. Perpendicular distance D from the curve point cp (xc p,y cp )to the perpendicular bisector is given by the equation:
y D = cp M x cp ( y1 y2) ( x1 x2) + + M 2 ( 1+ M M ) + 2. (5) Fig. [5]. 1. Initial and final curve points of the fingers 2. Tip and valley ends. Once the tip and valley ends are obtained, various features of the hand are extracted from these points. Figure [6] depicts the 22 features that we have considered for verification. As can be seen from the features the orientation of the hand does not effect the feature extraction. Small anomalies of the hand like bulbs in the hand are eliminated by suitably selecting the θ th and the offset for the angle calculation at the contour point p. Fig. [6]. Features of the hand The basic requirement for feature extraction procedure is that the fingers should be clearly separated from one another. The lengths and widths of the hand are not ef-
fected by the orientation of the hand and also the distance between the valley and tip ends as shown in figure[6], does no change with the placement and orientation of the hand. These features are extracted and are stored as feature vector in a file which is used for matching. 5. Verification: Characteristics of various features are taken into consideration for verification. Each feature will be assigned a weight depending on the variance of the feature due to placement of the hand. For example the base width of a finger is assigned lower weight, since the width may change slightly (5%) when the user over-stretches his hand and also the length of the thumb changes when the user relaxes his hand during verification procedure. The heights of four fingers and the distance between the valley ends and tip ends remain constant in all circumstances, so they are assigned higher weights. The verification procedure involves assigning weights to the various features and matching with the three pre-stored user s feature templates. Matching is considered successful if the weighted value is greater than a preset threshold value M th. M th is selected based on optimum FAR-FRR ratio. 6. Conclusion and Future work We have developed a peg-free hand-geometry verification system that is independent of orientation and placement of the hand. The system was experimented with a database consisting of 250 images collected over time from 25 users. 10 sample images from each user were used for verification purpose. Around 300 images were collected, out of which only 250 images were experimented on, and the rest were left out due to improper placements of hand by the user as shown in fig[7]. The verification system extracts the feature vector from the image and stores the template for later verification. Genuine match score is obtained by comparing the two feature vectors of the same hand and imposter match score is obtained by comparing the feature vectors of two different hands.
Fig. [7]. Improper placements of the hand The results of the experiment show that hand-geometry based verification system can be used for access control in low-medium security zones and can also be combined with other forms of biometrics like finger print to increase the confidence levels in very high security zones. Our future attempts include the use of curve-fitting techniques to extract the shape of the palm and the segmented fingers and include them to our feature vector and make the hand geometry verification system more robust and error free. We also plan to train the system with real time images of the user s hand to update the template database to avoid errors resulting from feature changes due to aging, for better performance during the long run. 8. References [1] Jain, A.K., Ross, A. and Pankanti, S., A Prototype Hand Geometry-Based Verification System, Proceedings of Second International Conference on Audio- and Video-based Biometric Person Authentication, Washingt on D.C., USA, pp. 166-171, 1999. [2] A.K. Jain, R. Bolle and S. Pankanti (Eds.), Biometrics: Personal Identification in Networked Society", Kluwer Academic Publishers, 1998. [3]Yaroslav Bulatov, Sachin Jambawalikar, Piyush Kumar, Surabh Sethia, Hand recognition using geometric classifiers, DIMACS Workshop on Computational Geometry, Rutgers University, Piscataway, NJ, November 14-15, 2002. [4]Anil K. Jain and Nicolae Duta. Deformable matching of hand shapes for verification. In Proceedings of International Conference on Image Processing, October 1999. [5] L. Wong and P. Shi, "Peg-Free Hand Geometry Recognition Using Hierarchical Geometry and Shape Matching", IAPR Workshop on Machine Vision Applications, Nara, Japan, December, 2002. Pp. 281-284.
[6] Cenker Oden, Aytul Ercil, Burak Buke, " Combining implicit polynomials and geometric features for hand recognition", Audio- and video-based biometric person authentication (AVBPA), September 2003. Pages: 2145-2152. [7] R. Sanchez-Reillo, Hand geometry pattern recognition through Gaussian mixture modeling, in 15th International Conference on Pattern Recognition, Vol.2, Sep, 2000. Pp.937-940. [8] V. Govindaraju, Z. Shi and J. Schneider, "Feature Extraction Using a Chaincoded Representation of Fingerprint Images", International conference on Audio- and videobased biometric person authentication (AVBPA), surrey, UK, 2003