Face recognition is nowadays an important issue due to its potential applications in different areas:
- Information security
- Law enforcement and surveillance
- Smart interfaces (laptops, phones, cards etc.)
- Access control
- and so forth
Despite the huge interest and the corresponding research effort, human face imagery classification is still an open topic.
Cătălin Căleanu, De-Shuang Huang, Vasile Gui, Virgil Tiponuţ, Valentin Maranescu,
Interest Operator versus Gabor filtering for facial imagery classification,
Pattern Recognition Letters,
Volume 28, Issue 8,
Previous work has shown that Gabor feature extraction is one of the most effective techniques employed for the human face recognition problem. However, the selection of a particular set of Gabor filters is often problematic and, also the computational requirements are considerable. We propose an alternative feature extraction method – the Interest Operator – to be applied for the facial recognition problem. This method has already been successfully used in the mobile robots navigation, stereoscopic vision and automatic target recognition. Experimental results presented in this paper indicate that classifiers, both neural (Multi-layer Perceptron) and statistical (k Nearest Neighbour), using the Interest Operator – based feature extraction, are capable to achieve almost the same classification rate as the Gabor-wavelet-based methods but in one order of magnitude lower processing time.
A special care has been put on the selection of the feature extraction filters and classifiers parameters.
Then, on AT&T public facial database, the system has achieved an average recognition rate of 95.2% using Gabor Approach and 94.7% using the Interest Operator.
Combined pattern search optimization of feature extraction and classiﬁcation parameters in facial recognition
Cătălin-Daniel Căleanu, Xia Mao, Gilbert Pradel, Sorin Moga, Yuli Xue,
Combined pattern search optimization of feature extraction and classification parameters in facial recognition,
Pattern Recognition Letters,
Volume 32, Issue 9,
Constantly, the assumption is made that there is an independent contribution of the individual feature extraction and classifier parameters to the recognition performance. In our approach, the problems of feature extraction and classifier design are viewed together as a single matter of estimating the optimal parameters from limited data. We propose, for the problem of facial recognition, a combination between an Interest Operator based feature extraction technique and a k-NN statistical classifier having the parameters determined using a pattern search based optimization technique. This approach enables us to achieve both higher classification accuracy and faster processing time.
► Feature extraction and classifier design treated as a single problem. ► The optimization technique employed represents a variant of Pattern Search. ► Error rates on AT&T and UMIST databases: 2.9% respectively 1.9%.