Two-phase linear reconstruction measure-based classification for face recognition

The principal aim was the construction of a face recognition system in order to be implemented in the service robot Donaxi, delimited by the Who is who test which is part of the RoboCups tests set, using an evolutionary development strategy of triple iterations. A two phase hybrid algorithm was developed, the first phase aim was the face detection using the Haar classifiers for face search in an image and the second phase is based on a decision tree whereby the faces characteristics were evaluated by the comparison techniques of phase correlation and histogram comparison. The needed characteristics were identified in order to develop this work as a software engineering project which allowed the algorithm construction and implementation through an evolutionary approach and a personal development process. The evolutionary strategy allowed the prototyping development with functionality and the tracking of the final system construction. A three iterations total was realized during which the needed metrics were registered (time, defects and sizes). The final analysis of results (algorithm and methods) allowed concluding and visualizing the employment advantages of a software engineering formal technique for research and robotics projects realization when improving estimations and software production quality.

Download Full-text

Distance approximation for two-phase test sample representation in face recognition

Neural Computing and Applications ◽

10.1007/s00521-013-1352-8 ◽

2013 ◽

Vol 24 (6) ◽

pp. 1341-1353 ◽

Cited By ~ 2

Author(s):

Xiang Wu ◽

Ning Wu

Keyword(s):

Face Recognition ◽

Test Sample ◽

Two Phase ◽

Distance Approximation ◽

Sample Representation

Download Full-text

A Collaborative Representation Based Two-Phase Face Recognition Algorithm

2013 Second International Conference on Robot, Vision and Signal Processing ◽

10.1109/rvsp.2013.12 ◽

2013 ◽

Author(s):

Zhengming Li ◽

Gaoyuan Liu

Keyword(s):

Face Recognition ◽

Recognition Algorithm ◽

Collaborative Representation ◽

Two Phase

Download Full-text

A Kernel Two-Phase Test Sample Sparse Representation for Face Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416560012 ◽

2015 ◽

Vol 30 (01) ◽

pp. 1656001

Author(s):

Zhonghua Liu ◽

Jiexin Pu ◽

Yong Qiu ◽

Moli Zhang ◽

Xiaoli Zhang ◽

...

Keyword(s):

Face Recognition ◽

Sparse Representation ◽

Recognition Performance ◽

Mapping Function ◽

Feature Space ◽

Test Sample ◽

Training Data ◽

Two Phase ◽

Kernel Parameter ◽

Representation Method

Sparse representation is a new hot technique in recent years. The two-phase test sample sparse representation method (TPTSSR) achieved an excellent performance in face recognition. In this paper, a kernel two-phase test sample sparse representation method (KTPTSSR) is proposed. Firstly, the input data are mapped into an implicit high-dimensional feature space by a nonlinear mapping function. Secondly, the data are analyzed by means of the TPTSSR method in the feature space. If an appropriate kernel function and the corresponding kernel parameter are selected, a test sample can be accurately represented as the linear combination of the training data with the same label information of the test sample. Therefore, the proposed method could have better recognition performance than TPTSSR. Experiments on the face databases demonstrate the effectiveness of our methods.

Download Full-text