by hand is a simple, easy to use and cheap technique, so it is used at hundreds of locations throughout ... â¢Hand Registration. â¢Localization of Hand Extremities.
Multimodal Biometric Verification and Identification Using Face and Hand Elif S¨urer, Helin Duta˘gacı, Berk G¨okberk, B¨ulent Sankur, Lale Akarun ˙ Bo˘gazi¸ci University, Istanbul (Turkey), {elifsu,dutagach,gokberk,bulent.sankur,akarun}@boun.edu.tr
Biometric Identifiers
Multimodal Flow Diagram
Several biometric identifiers are available. In this study, two of the available approaches have been used:
Datasets Used In Testing • Dataset-1: Pictures of 40 people taken with the implemented system. Each of the users has 4 pictures in training set.
• Face: Face recognition approaches can be classified as the ones focusing on facial attributes such as the eyes, eyebrows, nose, lips, chin, and the ones focusing on overall face image and a combination of both.
• Dataset-2: A subset of Dataset-1 where each person has 2 pictures in training set. • Dataset-3: A Chimeric combination of 146 people whose face data is from FERET database and hand data is from a previous study of Yoruk et al.
• Hand Geometry: Hand shape and size of fingers are also regarded as biometric characteristics. Recognition by hand is a simple, easy to use and cheap technique, so it is used at hundreds of locations throughout the world.
Difficulties In Testing System Implemented • Illumination conditions The packages and platforms used: • Java(J2SE)
• Finding subjects • Constraints of hand that enable acquisition with camera
• Matlab R2007a • MySQL 5.0 • FMJ (Freedom for Media in Java) • JMatIO
Recognition Results
• JGoodies • Dataset-1
Face Recognition Method 2D Gabor Wavelets:
User Interface
• Similar to cells in visual cortex • Multi-orientation & Multi-resolution • Sparse sampling Gabor Kernel Convolution: • Placing a grid on the face • Choosing the optimal mask (11x11) • Convolution with the Gabor mask • Linear Normalization
Available user interface screens are: • Camera Setup & Settings • Initial Screen • Enrollment Screen • Current View Snapshot • Welcome Screen • Recognition Screen
G¨okberk B., L. Akarun, and E. Alpaydin, “Feature Selection for Pose Invariant Face Recognition”, Proceedings of the 16th International Conference on Pattern Recognition, 2002, pp. 306-309, Quebec City, Canada.
Hand Recognition Method Hand Geometry: • Segmentation • Hand Registration • Localization of Hand Extremities • Ring Artifact Removal • Finger Registration • Feature Extracttion (ICA)
Screen Shots
– Face 95%. 2 out of 40 people have been confused. – Hand 90%. 4 out of 40 people have been confused. – Fusion of Face and Hand ∗ Borda Count 100%. ∗ Fixed-Arithmetic Combination 95%. 2 out of 40 people have been confused. ∗ Confidence Aided Fusion 95%. 2 out of 40 people have been confused. • Dataset-2 – Face 95%. 2 out of 40 people have been confused. – Hand 82.5%. 7 out of 40 people have been confused. – Fusion of Face and Hand ∗ Borda Count 100%. ∗ Fixed-Arithmetic Combination 90%. 4 out of 40 people have been confused. ∗ Confidence Aided Fusion 87.5%. 5 out of 40 people have been confused. • Dataset-3 – Face 93.15%. 10 out of 146 people have been confused. – Hand 99.32%. 1 out of 146 people has been confused. – Fusion of Face and Hand ∗ Borda Count 99.32%. 1 out of 146 people has been confused. ∗ Fixed-Arithmetic Combination 95.21%. 7 out of 146 people have been confused. ∗ Confidence Aided Fusion 96.6%. 5 out of 146 people have been confused.
Y¨or¨uk E., E.Konuko˘glu, B. Sankur and J. Darbon, “Person Authentication Based On Hand Shape”, Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, 2004.
Usability Results Fusion Methods The following methods have been used in the fusion of face and hand geometry: • Sequential Fusion • Borda Count • Fixed-Arithmetic Combination • Confidence-Aided Fusion
• A short questionnaire to evaluate the ease of use, speed and user interface of the system has been provided to 40 people. • User interface has been modified in terms of the suggestions. Face Avg. Face(%) Hand Avg. Hand (%) Ease of Use 4.075 81.5 3.55 71 Speed 4.125 82.5 3.9 78 UI 4.2 84 4.15 83
