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Soft Biometrics at CUBS Venu Govindaraju CUBS, University at Buffalo

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Presentation on theme: "Soft Biometrics at CUBS Venu Govindaraju CUBS, University at Buffalo"— Presentation transcript:

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2 Soft Biometrics at CUBS Venu Govindaraju CUBS, University at Buffalo

3 Background  Traits of biometrics  Universality  Distinctiveness  Permanence  Collectability  Acceptability  Present perfect?  No biometric is truly universal. It is estimated that 2- 4% of the population have unusable fingerprints  Each biometric has a lower bound for errors (constraint of algorithm + individuality)  Individual biometrics need to be augmented by other biometrics (multi-modal) or traits (soft biometrics)

4 Soft Biometrics  Not very distinctive  Can be used to augment regular biometrics  Not typically used during verification/identification  More intuitive than strong biometrics Definition[1]  Soft biometric traits are those characteristics that provide some information about the individual but are not distinctive enough to sufficiently differentiate any two individuals [1] [1] A. K. Jain, S. Dass, K. Nandakumar, “Soft Biometrics for Personal Identification”, SPIE Defense and Security Symposium 2003

5 Soft Biometrics : Examples Other classification  Continuous: Age, Height, Weight etc.  Discrete: Gender, Eye Color, Ethnicity etc.

6 Motivation  Heckathorn[3] have shown that a combination of personal attributes can be used to identify the individual reliably  Binning and Indexing  Hardening primary biometric  Speech Recognition  Can be used to tune individual biometrics  Socially aware computing (call centers)?

7 Extracting Soft Biometric Traits  Devices  Color video  Stereo images  Challenges  Controlled vs Uncontrolled environment  Pose variations  Illumination variation  Complex backgrounds  Feature selection and extraction  Features used in traditional biometrics do not encode soft biometric traits  Decision systems (soft thresholds)

8 Problems in Representation Purely statistical features Fuzzy class boundaries

9 Soft Biometrics Research at CUBS  Speech  Gender Identification  Accent Identification  Face  Face Catalog: Semantic Face Retrieval  Gender Classification  Skin  Skin spectroscopy

10 Soft Biometric Traits in Speech  Gender  There exists a difference in the pitch period between genders  This difference is fundamental in the discrimination between males and females  Accent [1]  Temporal features: onset time, closure/voicing/word duration  Prosodic/Intonation slope patterns  Formant frequencies  Age  The average power measurement and speech rate are used as indicators for measurement of agedness in a speaker [1]A Study of Temporal Features and Frequency characteristics in American English Foreign Accent L.M. Arslan, J.H.L. Hansen, Journal of the Acoustical society of America, July 1997

11 Uses of Soft Biometrics in Speech Soft Biometrics for binning Primary Biometric Soft Biometric(s) P(w|x 1 )P(w|x 1 y) Soft Biometrics for improving accuracy

12 Loose Gender Classification (PITCH)  3 Methods  Fast Fourier Transform  Linear Predictive Analysis  Cepstral Analysis  Data  75 files  Males -41, Females -34 Male LowMale Medium Male High Female Low Female Medium Female High 132Hz 156Hz 171Hz 205Hz 230Hz 287Hz Results

13 Definition of Accent (linguistics)  An accent is the perceived peculiarities of pronunciation and intonation of a speaker or group of speakers  A foreign accent is defined in a way that the phonology of the spoken language is modified by the phonology of another language, more familiar to the speaker  3 major language groups  American  Chinese  Indian

14 Proposed Approach for Accent  First identify the accent markers  Determine the effect of gender and co-articulation  Initially develop a text dependent model  Accumulate evidence over time  Features:  formants  phoneme duration  instantaneous (mel)cepstral slopes  HMMs

15 Accent Markers  A look at various non-native pronunciations of English  CHINESE  ‘r’ read sometimes as ‘l’ or ‘w’  ‘v’ read as ‘w’  ‘th’ read as ‘d’  ‘n’ and ‘l’ often confused  Often drop articles like ‘the’ and ‘a’  INDIAN SUBCONTINENT  Use of the rhotic ‘r’  Use of rolling ‘l’  Fast speech tempo with choppy syllables  Rhythmic variation of pitch Webster’s Revised Unabridged Dictionary Definition of non-native pronunciations of English – wordIQ.com

16 F2 F3 PLEASESTELLA SLABSPLASTIC MALES – PHONEME CONTAINING ‘L’ American - Indian -

17 F3 F2 BRING F3 F2 RED F3 F2 FRESH MALES – PHONEMES CONTAINING ‘R’ AND ‘AA’ F2 F3 ASK American - Indian -

18 FEMALES – SEGMENTED PHONEMES ‘L’, ‘R’, ‘AA’ F3 F2 PLEASE F3 F2 STELLA F3 F2 RED F3 F2 ASK American - Indian -

19 Soft Biometrics for Law Enforcement Novel Forensic System

20 Law Enforcement Application: Face Catalog  User can select some facial feature to describe.  System will prompt the user after each query with the best feature for the next query.

21 Related Work  Identikit [1] composes faces by putting together transparencies of facial features.  Evofit [2], automate the process of identikits.  Phanthomas [3] face composition using elastic graph matching.  CAFIIRIS [4] and Photobook [5] use PCA for face composition and matching.  But general description of users are semantic! 1.V. Bruce, “Recognizing Faces”, Faces as Patterns, pp , Lawrence Earlbaum Associates, Frowd, C.D., Hancock, P.J.B., & Carson, D. (2004). “EvoFIT: A Holistic, Evolutionary Facial Imaging Technique for Creating Composites”, ACM TAP, Vol. 1 (1) 3.“Phantomas: Elaborate Face Recognition “.Product description: solutions.com/FaceRecognition.htm 4.J. K. Wu, Y. H. Ang, P. C. Lam, S. K. Moorthy, A. D. Narasimhalu, ”Facial Image Retrieval, Identification, and Inference System” 5.A. Pentland, R. Picard, S. Sclaroff, “Photobook: tools for content based manipulation of image databases”, Proc. SPIE: Storage and Retrieval for Image and Video Databases II, vol. 2185

22 Face Catalog System Overview Face Detection Lip Location and parameterization Eye Location Parameterization of other Features Query Sub-System Prompting Sub-System Semantic Description Face Image Database Meta Database Input Image Sorted Images user Semantic Face Retrieval System

23 Enrollment Sub-System  Face Detection.  Lips and eye detection.  Locate and parameterize other features.

24 Query Sub-System  Pruning images based on descriptions given?  What if user makes a mistake in one of the description.  Ranking images based on their probability of being the required person is a better idea.  Bayesian learning can be used to update probability of each face being the required one.  Prompting users the feature with highest entropy at each step.

25 Example Query Query = [] Query = [Spectacles = Yes] Query = [Spectacles = Yes + Mustache = Yes] Query = [Spectacles = Yes + Mustache = Yes + Nose = Big] Probabilities of Faces

26 Results  Results of Enrollment Sub-system (Database of 150 images)  Results of Query (25 users, 125 test cases) Top 5Top 10Top 15 Average no. of queries FeaturesNumber of False Accepts Number of False Rejects Spectacles 12 Mustache 24 Beard 40 Long Hair 28 Balding 10

27 Gender Classification in Images  Gender classification  Identifying male or female from facial image  Existing approaches  Geometric feature based [1]-[2]  Appearance feature based (raw data feature or PCA + classifier) [3]  Approaches using other features, e.g., wrinkle and skin color [4] [1] A. Burton, V. Bruce and N. Dench, “What’s the difference between men and women? Evidence from facial measurements,” Perception, vol. 22, pp , [2]R. Brunelli and T. Poggio, “Hyperbf network for gender classification,” DARPA Image Understanding Workshop, pp , [3]B.A. Golomb, D.T. Lawrence, T.J. Sejnowski, “Sexnet: A Neural Network Identifies Sex from Human Faces,” Advances in Neural Information Processing Systems3, R.P Lippmann, J.E. Moody, D.S. Touretzky, eds. Pp , [4] J. Hayashi, M. Yasumoto, H. Ito, H. Koshimizu, “Age and gender estimation based on wrinkle texture and color of facial images,”, Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp , Aug. 2002

28 Gabor Feature based gender classification system Feature Extractor Using Gabor Wavelet SVM Classifier Preprocessing (Face detection, normalization, etc.) Raw Image Decision

29 Facial image Normalization  Mapping feature points to fixed positions  Feature points  Centers of two pupils  Tip of the nose  Normalized image  64 by 64  Convert from color to grayscale by averaging RGB components

30 Gabor feature  Gabor filter and Gabor wavelet [B.S. Manjunath, et al, PAMI, 1996] Gabor Filter: Gabor Wavelet: Fourier Transform of g(x, y):

31  Redundancy reduction [B.S. Manjunath, et al, PAMI, 1996]  Let and denote the lowest and highest frequencies of interest  are determined by Gabor feature (cont.)

32  Characteristics of Gabor wavelet  A powerful tool to capture changes of signals  Selective on certain frequency and orientation by setting parameters m, n  Gabor feature for gender classification  Gabor WT at 4 scalses, 4 orientations (m = 0,.., 3; n = 0, …, 3)  Each output image of Gabor WT (64 by 64) is divided into non- overlapping blocks of the size 2 m+2 by 2 m+2 (m: the scale number).  Average of magnitudes in each block as a feature  Total number of features

33 Gabor feature (cont.)

34 Classification  Features  1360-dimensional training and testing vectors fed into SVM classifier  Classifier  SVM with Gaussian RBF kernel [6] (B. Moghaddam, et al, PAMI 2002)  Adjust γ to minimize error rate  1360 features from Gabor WT (in 4 scales, 4 orientations) of 64×64 input image  Training and testing vectors (of 1360 dimensions) normalized into unit vectors

35 Experimental Results  Dataset: AR face database [A.M. Martinez and R. Benavente, “The AR face database,” CVC Tech. Report #24, 1998]  Overall: 3265 frontal facial images including 136 Caucasian people (768 by 576, color)  Training: 2246 samples including 91 individuals  Testing: 1019 samples including 45 individuals  Test #1  393 regular samples. Accuracy: 96.2%  Test #2  626 irregular samples (occluded by dark sun-glasses or masks) Accuracy: 92.7% MethodAccuracy of test #1Accuracy of test #2 Gabor feature + SVM with Gaussian RBF kernel 96.2%92.7% Raw data feature + SVM with Gaussian RBF kernel 94.7%89.8%

36 Skin Spectroscopy  Measures the composition of the skin using IR(Deep tissue biometric)  Based on spectroscopy  Fool proof against fake fingers (Can detect liveness)  Can be easily integrated into solid state devices  Immune to surface degradations  Currently implemented by only one Vendor (Lumidigm Inc) Skin composition

37 Chromophores in skin  Melanin  Absorbs light at all wavelengths  Absorbance decreases with increase in wavelength  Hemoglobin  Strongest absorption bands in 405 – 430 nm and 540 – 580 nm.  Lowest absorption beyond 620 nm  Can be used for liveness testing  Collagen, Keratin, Carotene

38 Spectra of Melanin and Hemoglobin

39 Sample Skin Spectrum

40 Sample skin spectrum (contd.)

41

42 Results so far  Soft classification based on skin color  Melanin index used as indicator of skin color  Spectral difference noticed between different skin locations on the same individual

43 Thank You


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