Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

https://www.koreascience.or.kr:443/article/JAKO200920237949770.pdf

A Survey of Face Recognition Techniques

We present a novel face representation based on locally adaptive regression kernel (LARK) descriptors. Our LARK descriptor measures a self-similarity based on “signal-induced distance” between a center pixel and surrounding pixels in a local neighborhood. By applying principal component analysis (PCA) and a logistic function to LARK consecutively, we develop a new binary-like face representation which achieves state-of-the-art face verification performance on the challenging benchmark “Labeled Faces in the Wild” (LFW) dataset. In the case where training data are available, we employ one-shot similarity (OSS) based on linear discriminant analysis (LDA). The proposed approach achieves state-of-the-art performance on both the unsupervised setting and the image restrictive training setting (72.23% and 78.90% verification rates), respectively, as a single descriptor representation, with no preprocessing step. As opposed to combined 30 distances which achieve 85.13%, we achieve comparable performance (85.1%) with only 14 distances while significantly reducing computational complexity.

/pdf/face-verification-using-the-lark-representation-3qbc5j5kef.pdf

Face Verification Using the LARK Representation

A note on two-dimensional linear discriminant analysis

Face recognition has the wide research and applications on many areas. Many surveys of face recognition are implemented. Different from previous surveys on from a single viewpoint of application, method or condition, this paper has a comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions. In the sensory inputs, we review face recognition from image-based, video-based, 3D-based and hypersprectral image based face recognition, and a comprehensive survey of face recognition methods from the viewpoints of signal processing and machine learning are implemented, such as kernel learning, manifold learning method. Moreover we discuss the single training sample based face recognition and under the variable poses. The prominent algorithms are described and critically analyzed, and relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed

Multiple viewpoints based overview for face recognition

Biometric recognition have been getting popular in recent years. In this paper one of the biometric recognition techniques, face recognition, is purposed by using windowing feature extraction method and artificial neural network classifier. In the paper, ORL database which consist of ten images of forty people is used to test our software and method. First of all, images are separated to the different size windows, 4 by 4 and 8 by 8. Then, it is obtained the means of each window and totally sixteen by one and sixty four by one vectors features are obtained, respectively. According to the created features of each images, Artificial Neural Network (ANN) is trained by using different learning rate, momentum factor etc. Finally, the network is tested as to testing values and it’s observed the remarkable results of the study. As it expected, the methods which separate the images 8 by 8 is more successful than the other one. On the other hand, 4 by 4 windowing feature have also remarkable results, although it has less features. 

http://www.joig.org/uploadfile/2016/0603/20160603075500991.pdf

Face Recognition by Using Back Propagation Artificial Neural Network and Windowing Method

Volume measure in 2DPCA-based face recognition

Face symmetrical feature can be applied to two-dimensional principal component analysis (2DPCA) for face image feature extraction, and this procedure can be called symmetrical 2DPCA (S2DPCA). Now, these S2DPCA-based face recognition algorithms almost pay much attention to the feature extraction, and the classification measures have been little investigated. In this paper, the typical similarity measure used in 2DPCA is applied to S2DPCA, which is the sum of the Euclidean distance between two feature vectors in feature matrix, called distance measure (DM). The similarity measure based on Frobenius-norm is also developed to classify face images for S2DPCA. Furthermore, the relative theories on S2DCPA are proofed. The experimental results on ORL and FERET face databases show that S2DPCA has the potential to outperform traditional 2DPCA, especially on condition that DM is used for S2DPCA.

Ji-Cheng Meng

Papers

Volume measure in 2DPCA-based face recognition

S2DPCA with DM and FM in Face Recognition