Himanshu S. Bhatt

Plastic Surgery: A New Dimension to Face Recognition

Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem.

Periocular biometrics: When iris recognition fails

The performance of iris recognition is affected if iris is captured at a distance. Further, images captured in visible spectrum are more susceptible to noise than if captured in near infrared spectrum. This research proposes periocular biometrics as an alternative to iris recognition if the iris images are captured at a distance. We propose a novel algorithm to recognize periocular images in visible spectrum and study the effect of capture distance on the performance of periocular biometrics. The performance of the algorithm is evaluated on more than 11,000 images of the UBIRIS v2 database. The results show promise towards using periocular region for recognition when the information is not sufficient for iris recognition.

Recognizing Surgically Altered Face Images Using Multiobjective Evolutionary Algorithm

Widespread acceptability and use of biometrics for person authentication has instigated several techniques for evading identification. One such technique is altering facial appearance using surgical procedures that has raised a challenge for face recognition algorithms. Increasing popularity of plastic surgery and its effect on automatic face recognition has attracted attention from the research community. However, the nonlinear variations introduced by plastic surgery remain difficult to be modeled by existing face recognition systems. In this research, a multiobjective evolutionary granular algorithm is proposed to match face images before and after plastic surgery. The algorithm first generates non-disjoint face granules at multiple levels of granularity. The granular information is assimilated using a multiobjective genetic approach that simultaneously optimizes the selection of feature extractor for each face granule along with the weights of individual granules. On the plastic surgery face database, the proposed algorithm yields high identification accuracy as compared to existing algorithms and a commercial face recognition system.

On matching sketches with digital face images

This paper presents an efficient algorithm for matching sketches with digital face images. The algorithm extracts discriminating information present in local facial regions at different levels of granularity. Both sketches and digital images are decomposed into multi-resolution pyramid to conserve high frequency information which forms the discriminating facial patterns. Extended uniform circular local binary pattern based descriptors use these patterns to form a unique signature of the face image. Further, for matching, a genetic optimization based approach is proposed to find the optimum weights corresponding to each facial region. The information obtained from different levels of Laplacian pyramid are combined to improve the identification accuracy. Experimental results on sketch-digital image pairs from the CUHK and IIIT-D databases show that the proposed algorithm can provide better identification performance compared to existing algorithms.

Memetically Optimized MCWLD for Matching Sketches With Digital Face Images

One of the important cues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm to extract discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multiscale circular Webers local descriptor. Further, an evolutionary memetic optimization algorithm is proposed to assign optimal weight to every local facial region to boost the identification performance. Since forensic sketches or digital face images can be of poor quality, a preprocessing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.

Face recognition for newborns: A preliminary study

Newborn swapping and abduction is a global problem and traditional approaches such as ID bracelets and footprinting do not provide the required level of security. This paper introduces the concept of using face recognition for identifying newborns and presents an automatic face recognition algorithm. The proposed multiresolution algorithm extracts Speeded up robust features and local binary patterns from different levels of Gaussian pyramid. The feature descriptors obtained at each Gaussian level are combined using weighted sum rule. On a newborn face database of 34 babies, the proposed algorithm yields rank-1 identification accuracy of 86.9%.

On Recognizing Faces in Videos Using Clustering-Based Re-Ranking and Fusion

Due to widespread applications, availability of large intra-personal variations in video and limited information content in still images, video-based face recognition has gained significant attention. Unlike still face images, videos provide abundant information that can be leveraged to address variations in pose, illumination, and expression as well as enhance the face recognition performance. This paper presents a video-based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images from a large dictionary. A three-stage approach is proposed for optimizing ranked lists across multiple video frames and fusing them into a single composite ordered list to compute the video signature. This signature embeds diverse intra-personal variations and facilitates in matching two videos with large variations. For matching two videos, a discounted cumulative gain measure is utilized, which uses the ranking of images in the video signature as well as the usefulness of images in characterizing the individual in the video. The efficacy of the proposed algorithm is evaluated under different video-based face recognition scenarios such as matching still face images with videos and matching videos with videos. The efficacy of the proposed algorithm is demonstrated on the YouTube faces database and the MBGC v2 video challenge database that comprise different types of video-based face recognition challenges such as matching still face images with videos and matching videos with videos. Performance comparison with the benchmark results on both the databases and a commercial face recognition system shows the efficiency of the proposed algorithm for video-based face recognition.

Improving Cross-Resolution Face Matching Using Ensemble-Based Co-Transfer Learning

Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery.A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges.

Evolutionary granular approach for recognizing faces altered due to plastic surgery

Recognizing faces with altered appearances is a challenging task and is only now beginning to be addressed by researchers. The paper presents an evolutionary granular approach for matching face images that have been altered by plastic surgery procedures. The algorithm extracts discriminating information from non-disjoint face granules obtained at different levels of granularity. At the first level of granularity, both pre and post-surgery face images are processed by Gaussian and Laplacian operators to obtain face granules at varying resolutions. The second level of granularity divides face image into horizontal and vertical face granules of varying size and information content. At the third level of granularity, face image is tessellated into non-overlapping local facial regions. An evolutionary approach is proposed using genetic algorithm to simultaneously optimize the selection of feature extractor for each face granule along with finding optimal weights corresponding to each face granule for matching. Experiments on pre and post-plastic surgery face images show that the proposed algorithm provides at least 15% better identification performance as compared to other face recognition algorithms.

On co-training online biometric classifiers

In an operational biometric verification system, changes in biometric data over a period of time can affect the classification accuracy. Online learning has been used for updating the classifier decision boundary. However, this requires labeled data that is only available during new enrolments. This paper presents a biometric classifier update algorithm in which the classifier decision boundary is updated using both labeled enrolment instances and unlabeled probe instances. The proposed co-training online classifier update algorithm is presented as a semi-supervised learning task and is applied to a face verification application. Experiments indicate that the proposed algorithm improves the performance both in terms of classification accuracy and computational time.