The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >

Human and machine recognition of faces: a survey

http://www.cs.ait.ac.th/~mdailey/cvreadings/Bowyer-Face.pdf

A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition

Automatic recognition and analysis of human faces and facial expressions: a survey

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

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods.

The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

/pdf/three-dimensional-face-recognition-2x1qhnfg2z.pdf

Three-Dimensional Face Recognition

Work related to the authentification or recognition of human faces from their range images is described. The approach consists of comparing a part of the profile or the surface of two faces. For this purpose, the profile plane of each face is considered as a quasi-symmetry plane and is extracted by an iterative process which involves the similarity of the Gaussian curvature values of the face surface. Then an optimal matching of the two profiles is done, which allows the calculation of similarity indices of profiles or surfaces. Tests for robustness and the authentification or recognition results show the efficiency of the whole procedure. >

Face authentification or recognition by profile extraction from range images

A primal approach to the analysis of a human face through its 3-D image is proposed. This analysis is based on local pattern concepts deduced from the intrinsic properties of a surface described by its local curvature characteristics. Some theoretical results about curvature are recalled and then used for the extraction of characteristic geometrical features from the face surface. Recognition can be achieved by using a pattern vector of distances calculated from these features. Experimental results are provided to illustrate the various steps of the approach.

J. T. Lapresté

Papers

Face authentification or recognition by profile extraction from range images

Face recognition from range data by structural analysis