Narayan Vetrekar

Low-cost multi-spectral face imaging for robust face recognition

Multi-spectral face recognition has acquired significant attention over a last few decades due to its potential of capturing spatial and spectral information across the electromagnetic spectrum. In this paper, we present a new imaging scheme that can obtain the multi-spectral face image at nine different spectra covering 530nm-1000nm. We prepared a new database comprising of 230 subjects using our new low-cost multi-spectral face imaging device. Extensive experiments are presented for evaluating the performance of the four different state-of-the-art face recognition algorithms on both individual bands and the fused spectral face image. Obtained results show the improved face recognition performance of Log-Gabor features with Collaborative Representation (CRC) as the classifier.

Extended multi-spectral face recognition across two different age groups: an empirical study

Face recognition has attained a greater importance in bio-metric authentication due to its non-intrusive property of identifying individuals at varying stand-off distance. Face recognition based on multi-spectral imaging has recently gained prime importance due to its ability to capture spatial and spectral information across the spectrum. Our first contribution in this paper is to use extended multi-spectral face recognition in two different age groups. The second contribution is to show empirically the performance of face recognition for two age groups. Thus, in this paper, we developed a multi-spectral imaging sensor to capture facial database for two different age groups (≤ 15years and ≥ 20years) at nine different spectral bands covering 530nm to 1000nm range. We then collected a new facial images corresponding to two different age groups comprises of 168 individuals. Extensive experimental evaluation is performed independently on two different age group databases using four different state-of-the-art face recognition algorithms. We evaluate the verification and identification rate across individual spectral bands and fused spectral band for two age groups. The obtained evaluation results shows higher recognition rate for age groups ≥ 20years than ≤ 15years, which indicates the variation in face recognition across the different age groups.

Human tissue diagnostics for physiological information using Hyperspectral Image model

Hyperspectral imaging is a novel technology for obtaining both spatial and spectral information in the area of aerospace and military industries for last 20 years. The multifusion, multispectral, hyperspectral and polarimetric imaging can provide both anatomical and physiological or metabolic information; it can play an important role in the development of molecular imaging technologies with enhanced specificity and sensitivity, capable of identifying the presence versus absence of cancer, detection of margins, stage, distribution, and type of cancer, but most importantly, techniques capable of assessing the progress of the disease and its response to treatment. Hyperspectral Image modeling provides practical alternatives for the field measurements in human tissue sample due to sample coupling problems and heavy absorption due to protein and water in UV and NIR regions respectively in the region of therapeutic window. In this paper we have proposed the Lorentz Oscillators techniques to generate the multivariate signal for a pixel signature over spectrum channel of interest. The model has been demonstrated to synthesize the image with proper signature for the pixel. The spectra having customized signatures are generated using proper values of the central frequency, Strength of Oscillators and Width of oscillators. These models can be customized for the specific vision under observation and serialized for the quality, intrusion or diagnostics using pattern recognition principle for both anatomical and physiological information in human tissue.

Collaborative representation of Grassmann manifold projection metric for robust multi-spectral face recognition

Spectral face recognition is gaining importance as the information from different bands can lead to robust face representation that are presentation (a.k.a, spoofing) attack resistant. However, the key challenge here is to process the high dimensional spatio-spectral data to extract and represent the reliable data while discarding redundant data information for processing. In this work, we present a new approach to represent the spectral images in a holistic manner by employing the well-known Projection Metric Learning (PML) in Grassmann manifold such that the redundant information from the spectral data is discarded while retaining significant information. The approach is adopted to learn discriminative information from an high dimensional spectral dataset. Further, we propose an extension using collaborative representation of learnt projection metrics for improving the classification accuracy of spectral data. With the extensive set of experiments conducted on a relatively large scale extended-spectral face image database (6048 images) of 168 subjects, we demonstrate the applicability of the proposed framework. The obtained results indicates highest accuracy by achieving a Rank-1 recognition rate of 98.21% in classifying the spectral face images.

Extended multi-spectral imaging for gender classification based on image set

Gender prediction based on facial features has received significant attention in computer vision and biometric community. Most of the gender classification studies mainly focused there attention on approaches that operate in the visible spectrum. In this paper we present gender classification using extended multi-spectral face data captured in nine narrow spectral bands across the visible near infrared spectrum (530nm to 1000nm). Further, we present the proposed method, that learns for this image set the discriminative spectral band features in the affine space and then classifies the features with a Support Vector Machine (SVM) in a robust manner. The extensive experimental results are presented on the reasonable sample size of 78300 spectral band images using our proposed method. The obtained results shows 90.49±3.56% average classification accuracy, indicating the applicability of our proposed method for gender classification.

Band level fusion using quaternion representation for extended multi-spectral face recognition

With the availability of sensor technology across the broad electromagnetic spectrum, multi-spectral imaging is increasingly used in biometric systems. Especially for face recognition, multi-spectral imaging has gained a lot of attention due to its invariant property against variation caused by unknown illumination. However, obtaining best performance using multi-spectral imaging is still a challenge due to presence of a modality gap between the spectral imaging data and redundant band information. In this paper, we propose a fused band representation with a set of selected bands represented in Quaternion space for spectral band images to efficiently maintain the inter band relationship in spatial domain. The selection is based on measuring the information content in bands using entropy and fusion is carried out in Quaternion space for three best bands. The features from newly obtained image is collaboratively represented to achieve robust performance. The proposed approach is experimentally validated on the extended multi-spectral face database of 168 subjects, whose spectral band images are captured in 9 narrow spectral bands in visible and near infrared range (530nm to 1000nm). The quantitative performance analysis, obtained using the proposed method indicates 96.13% recognition rate at Rank-1, outperforming other state-of-the-art methods.

Extended Spectral to Visible Comparison Based on Spectral Band Selection Method for Robust Face Recognition

Multi-spectral imaging has recently acquired significant attention in biometrics based authentication due to it’s potential ability to capture spatio-spectral images across the electromagnetic spectrum. Especially, in the case of facial biometrics, multi-spectral imaging has shown significant promising results under unknown/varying illumination environment. However, the challenge arises when surveillance cameras provide the visible images while the enrollment are spectral band images. In order to address the backward/cross compatibility of probing visible images from regular surveillance cameras against the high quality spectral band images in enrollment, development of robust algorithms are required. In this paper, we present a new approach of selecting optimal band based on highest correlation coefficients of individual feature vectors from bands in comparison with feature vectors from visible images of respective individual classes for robust recognition performance. The proposed approach of band selection is validated on a newly collected face database of 168 subjects whose face images are collected in 9 different spectral bands and correspondingly their visible images from a regular camera operating in visible spectrum. The extensive set of experiments conducted on the new database with selected single band and multiple spectral bands in enrollment data versus the visible probe image has indicated the significance of the band selection. The new approach of spectral to visible matching with the proposed band selection method shows significant Rank- 1 recognition rate of 94.04% supporting the applicability of proposed method

Photometric Normalization Techniques for Extended Multi-spectral Face Recognition: A Comparative Analysis

Biometric authentication based on face recognition acquired enormous attention due to its non-intrusive nature of image capture. Recently, with the advancement in sensor technology, face recognition based on Multi-spectral imaging has gained lot of popularity due to its potential of capturing discrete spatio-spectral images across the electromagnetic spectrum. Our contribution here is to study empirically, the extensive comparative performance analysis of 22 photometric illumination normalization techniques for robust Multi-spectral face recognition. To evaluate this study, we developed a Multi-spectral imaging sensor that can capture Multi-spectral facial images across nine different spectral band in the wavelength range from 530 nm to 1000 nm. With the developed sensor we captured Multi-spectral facial database for 231 individuals, which will be made available in the public domain for the researcher community. Further, quantitative experimental performance analysis in the form of identification rate at rank 1, was conducted on 22 photometric normalization techniques using four state-of-the-art face recognition algorithms. The performance analysis indicates outstanding results with utmost all of the photometric normalization techniques for six spectral bands such as 650 nm, 710 nm, 770 nm, 830 nm, 890 nm, 950 nm.

Experimental Evaluation of 3D Kinect Face Database

3D face recognition has gain a paramount importance over 2D due to its potential to address the limitations of 2D face recognition against the variation in facial poses, angles, occlusions etc. Research in 3D face recognition has accelerated in recent years due to the development of low cost 3D Kinect camera sensor. This has leads to the development of few RGB-D database across the world. Here in this paper we introduce the base results of our 3D facial database (GU-RGBD database) comprising variation in pose (0°, 45°, 90°, −45°, −90°), expression (smile, eyes closed), occlusion (half face covered with paper) and illumination variation using Kinect. We present a proposed noise removal non-linear interpolation filter for the patches present in the depth images. The results were obtained on three face recognition algorithms and fusion at matching score level for recognition and verification rate. The obtained results indicated that the performance with our proposed filter shows improvement over pose with score level fusion using sum rule.

Hyperspectral biometrics for facial mode: An alternate approach to multimode method

Use of Biometric systems has been increased in past decades due to increasing demands of security. The unimodal biometric system has to suffer from problems such as intra class variation, noise in the sensor data etc. This problem can be solved using multimodal biometric fusion. In this paper authors have compared the Hyperspectral and multimodal (fingerprint and face) fusion at matching score level. The Hyperspectral images were fused for combinations like 650+710nm, 650+710+770 nm and the performance parameter like False Non Match Rate (FNMR) and False Match Rate (FMR) have been performed. The performance parameters for Hyperspectral imagery outperform that of the facial mode for a single spectral band i.e. 650nm. Also parameters for the Hyperspectral fusion of 650+710nm, 650+710+770nm combinations spectral modes are promising and are comparable for higher order combination to that of multimodal fusion biometrics. It is observed that the Hyperspectral fusion for higher spectral bands combinations is linear improvements in Equal Error Rate (EER) percentage.