A framework for face classification under pose variations
01 Sep 2014-pp 1886-1891
TL;DR: 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 is proposed and matched with the actual frontal features.
Abstract: 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.
Citations
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TL;DR: The comparison with the exiting state-of-art methods for ORL, IITK, CVL, AR, CASIA-Face-V5, FERET and CAS-PEAL face databases, show the superiority of the proposed face recognition system.
35 citations
TL;DR: 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 propose Cancelable FaceHashing Technique.
Abstract: 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.
21 citations
TL;DR: 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.
Abstract: 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.
20 citations
Cites background from "A framework for face classification..."
...[75] (2014): Automated facial recognition from video frames with variations in pose and appearance is performed....
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01 Dec 2015
TL;DR: A novel method for face recognition system using challenging profile and frontal faces is proposed in this paper and has satisfying performance as compared to existing methods for IITK, CASIA-FACE-V5, LIBOR, ORL and Extended YALE-B face databases.
Abstract: 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.
12 citations
TL;DR: A secure face recognition system for the IoT-enabled Healthcare system has been proposed, where each registered person will be identified by his/her face biometric with strong template protection schemes.
Abstract: 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.
5 citations
References
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Book•
14 Dec 2009
TL;DR: This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants, and a systematic review of the basic definitions and properties of moments.
Abstract: Moments as projections of an images intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging. Key features: Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments. Considers invariants to traditional transforms translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants. Reviews and extends a recent field of invariants with respect to convolution/blurring. Introduces implicit moment invariants as a tool for recognizing elastically deformed objects. Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments. Offers comprehensive advice on the construction of various invariants illustrated with practical examples. Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course. Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.
589 citations
"A framework for face classification..." refers methods in this paper
...For feature extraction Hu, Zernike and Legendre moments are used [2] [6]....
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01 Jan 1994
TL;DR: The detailed error analysis involved in the moment method is discussed and several new techniques to increase the accuracy and efficiency of moment descriptor are proposed for image reconstruction from the orthogonal Legendre moments computed from discrete and noisy data.
534 citations
01 Dec 2013
TL;DR: A more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs is introduced and is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related applications.
Abstract: Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting However, with the introduction of databases such as LFW and Pubfigs, face recognition community is gradually shifting its focus on much more challenging unconstrained settings Since its introduction, LFW verification benchmark is getting a lot of attention with various researchers contributing towards state-of-the-results To further boost the unconstrained face recognition research, we introduce a more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs The database consists of 34512 faces of 100 known actors collected from approximately 103 Indian movies Unlike LFW and Pubfigs which used face detectors to automatically detect the faces from the web collection, faces in IMFDB are detected manually from all the movies Manual selection of faces from movies resulted in high degree of variability (in scale, pose, expression, illumination, age, occlusion, makeup) which one could ever see in natural world IMFDB is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related applications
94 citations
"A framework for face classification..." refers background in this paper
...With the introduction of Databases such as LFW, Pubfigs and IMFDB face recognition community is gradually shifting its focus on much more challenging unconstrained settings....
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...My unconstrained Database (MUDB) This paper introduces new unconstrained human face Database called as "My unconstrained Database" which is inspired from IMFDB [7]....
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...Also this paper introduces new unconstrained human face Database called as "My unconstrained Database" which is inspired from IMFDB [7]....
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...Also this paper introduces new unconstrained human face database called as "My unconstrained Database" which is inspired from IMFDB....
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TL;DR: A review of the typical algorithms that aim to overcome one of the main obstacles in the face recognition task, the variations in face pose, is presented in this article, where future research challenges in pose-invariant face recognition are also identified.
Abstract: In recent years, face recognition has attracted significant attention from the research and commercial communities. Because of the wide variation in face images, face recognition for real applications remains a very challenging problem. A large number of face recognition algorithms, along with their modifications, have been proposed over the past three decades. This paper presents a review of the typical algorithms that aim to overcome one of the main obstacles in the face recognition task, the variations in face pose. These algorithms are categorized and briefly described. Future research challenges in pose-invariant face recognition are also identified.
35 citations
01 Jan 2012
TL;DR: An efficient face recognition system which is invariant to pose is proposed which presents a transformation to generate features of the frontal face from a given posed image of a subject.
Abstract: This paper proposes an efficient face recognition system which is invariant to pose. It presents a transformation to generate features of the frontal face from a given posed image of a subject. The proposed system has been tested on three databases viz. IITK, FERET and CMU-PIE. It has been observed that it performs better than the existing well known system.
12 citations