A Multiple Eigenspaces Constructing Method and Its Application to Face Recognition

Li, Wu-Jun; Luo, Bin; Wang, Chongjun; Zhong, Xiangping; Chen, Zhao-Qian

doi:10.1007/11539117_9

Cited by 2 publications

(1 citation statement)

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“…It is broadly classified into two types namely geometric feature-based and appearance-based [6]. The facial parts are considered as geometrical parameters and are utilized by geometric feature-based methods, for instance, elastic bunch graph matching [7] and active appearance model [8], while in appearance-based methods intensity or intensity-derived parameters are utilized [1] [17]. In a given video fragment, the process that is performed is associated with the image and is repeated for every frame.…”

Section: Introductionmentioning

confidence: 99%

A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences

K¹

2013

IOSR-JCE

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Abstract:Video face recognition is a widely used method in which security is essential that recognizes the human faces from subjected videos. Unlike traditional methods, recent recognition methods consider practical constraints such as pose and illumination variations on the facial images. Our previous work also considers such constraints in which face recognition was performed on videos that were highly subjected pose and illumination variations. The method asserted good performance however; it suffers due to high computational cost. This work overcomes such drawback by proposing a simple face recognition technique in which a cost efficient Active Appearance Model (AAM) and lazy classification are deployed. The deployed AAM avoids nonlinear programming, which is the cornerstone for increased computational cost. Experimental results prove that the proposed method is better than the conventional technique in terms of recognition measures and computational cost.

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Section: Introductionmentioning

confidence: 99%