Considerations on extended feature vectors in automatic face recognition

This study proposes an iterative closest shape point (ICSP) registration method based on regional shape maps for 3D face recognition. A neutral expression image randomly selected from a face database is considered as the reference face. The point-to-point correspondences between the input face and the reference face are achieved by constructing the points’ regional shape maps. The distance between corresponding point pairs is then minimized by iterating through the correspondence findings and coordinate transformations. The vectors composed of the closest shape points obtained in the last iteration are regarded as the feature vectors of the input face. These 3D face feature vectors are finally used for both training and recognition using the Fisherface method. Experiments are conducted using the 3D face database maintained by the Chinese Academy of Science Institute of Automation (CASIA). The results show that the proposed method can effectively improve 3D face recognition performance.

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Kernel-based optimized feature vectors selection and discriminant analysis for face recognition

Object recognition supported by user interaction for service robots ◽

10.1109/icpr.2002.1048314 ◽

2003 ◽

Cited By ~ 3

Author(s):

Qingshan Liu ◽

Rui Huang ◽

Hanqing Lu ◽

Songde Ma

Keyword(s):

Face Recognition ◽

Discriminant Analysis ◽

Feature Vectors

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Performance Improvement of Face Recognition System using Selective Local Feature Vectors

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v10i2.3299 ◽

2013 ◽

Vol 10 (2) ◽

pp. 1330-1338

Author(s):

Vasudha S ◽

Neelamma K. Patil ◽

Dr. Lokesh R. Boregowda

Keyword(s):

Face Recognition ◽

Performance Improvement ◽

Recognition Rate ◽

Recognition System ◽

Image Splicing ◽

Feature Vectors ◽

Face Recognition System ◽

Prime Concern ◽

Wide Range ◽

Recognition Systems

Face recognition is one of the important applications of image processing and it has gained significant attention in wide range of law enforcement areas in which security is of prime concern. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges. Face recognition systems are impressively sensitive to appearance variations due to lighting, expression and aging. The major metric in modeling the performance of a face recognition system is its accuracy of recognition. This paper proposes a novel method which improves the recognition accuracy as well as avoids face datasets being tampered through image splicing techniques. Proposed method uses a non-statistical procedure which avoids training step for face samples thereby avoiding generalizability problem which is caused due to statistical learning procedure. This proposed method performs well with images with partial occlusion and images with lighting variations as the local patch of the face is divided into several different patches. The performance improvement is shown considerably high in terms of recognition rate and storage space by storing train images in compressed domain and selecting significant features from superset of feature vectors for actual recognition.

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