Face recognition using fusion of feature learning techniques

A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryption-decryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system.

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A Novel Cancelable FaceHashing Technique Based on Non-invertible Transformation with Encryption and Decryption Template

Automated facial identification and facial expression recognition have been topics of active research over the past few decades. Facial and expression recognition find applications in human-computer interfaces, subject tracking, real-time security surveillance systems and social networking. Several holistic and geometric methods have been developed to identify faces and expressions using public and local facial image databases. In this work we present the evolution in facial image data sets and the methodologies for facial identification and recognition of expressions such as anger, sadness, happiness, disgust, fear and surprise. We observe that most of the earlier methods for facial and expression recognition aimed at improving the recognition rates for facial feature-based methods using static images. However, the recent methodologies have shifted focus towards robust implementation of facial/expression recognition from large image databases that vary with space (gathered from the internet) and time (video recordings). The evolution trends in databases and methodologies for facial and expression recognition can be useful for assessing the next-generation topics that may have applications in security systems or personal identification systems that involve "Quantitative face" assessments.

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A Survey of the Trends in Facial and Expression Recognition Databases and Methods

A novel method for face recognition system using challenging profile and frontal faces is proposed in this paper. The proposed face recognition system consists of pre-processing, feature extraction and classification components. In this work, for pre-processing, the face region is extracted using facial landmark points, obtained by the tree structured part model. During feature extraction, SIFT descriptors are computed from the detected face region, and Spatial Pyramid Matching approach based on Locality constraints Linear Coding technique is employed for feature representation. Finally multi-class linear SVM classifier is employed to do the classification job. Extensive experimental results have been performed to show that the proposed algorithm has satisfying performance as compared to existing methods for IITK, CASIA-FACE-V5, LIBOR, ORL and Extended YALE-B face databases.

Biometric recognition system for challenging faces

In Healthcare, the Internet of Things (IoT) enabled surveillance cameras capture thousands of images every day, where face recognition provides reliable security as well as smart treatment through patient sentiment analysis, emotion detection, automated nurse calls, and hospital traffic systems. In this paper, a secure face recognition system for the IoT-enabled Healthcare system has been proposed. Here each registered person will be identified by his/her face biometric with strong template protection schemes. To protect the biometric information, three steps template protection techniques have been proposed: (i) Cancelable biometrics, (ii) BioCrypto-Circuit, and (iii) BioCrypto-Protection schemes. The performance of the proposed system has been tested on four benchmark face databases CVL, IITK, Casia-Face-v5, and FERET. The results of the proposed system are reported in terms of the correct recognition rate and the equal error rate. These performances have also been compared with some state-of-the-art methods with respect to each employed database, which shows the novelty of the proposed system.

A Secure Face Recognition for IoT-Enabled Healthcare System

Automatically verifying a person from a video frame or a digital image using computer application is known as a Face Recognition system. With changes in facial pose, face appearance changes drastically. Recognition of faces under pose variations is much more challenging. Now a day's recognizing human faces in unconstrained or wild environment is emerging as a critically important and technically challenging computer vision problem. Recently face recognition community is gradually shifting its focus on much more challenging unconstrained setting. A new unconstrained human face Database called as “My unconstrained Database” has been developed in this paper. A model based approach is used and the Moment based feature extraction techniques (Hu's, Zernike and Legendre Moments) are implemented on three different face databases containing different poses of the faces. This paper proposes a modified method called “Genetic Algorithm based Transfer Vectors” for generation of features of a frontal face from the features of different poses of image. Next, the generated frontal features are matched with the actual frontal features. Extracted feature are classified by three different methods: kNN classifier, LDA and Transform Vector with Distance Metric and finally Correct Recognition Rate is determined.

Jagdish P. Sarode

Papers

A framework for face classification under pose variations