Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Abstract

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.

3D Object Recognition Using Fast Overlapped Block Processing Technique

“…The global-based feature extraction approach is, to some extent, inaccurate for noisy environments, which highly impedes the characterization of efficient 3D object algorithms in more realistic settings. Moreover, the performance of 3D object recognition accuracy may be degraded in noisy environments [ 45 , 81 ]. Therefore, preprocessing for the 3D object becomes essential to mitigate the effect of noise but it may come at the expense of increasing the computation complexity.…”

“…To extract local features, most applications use a non-overlapping block-processing technique. On the other hand, overlapped block processing could enhance the accuracy of 3D object recognition [ 45 , 81 ]. Typically, the processing of the blocks in parallel will significantly raise the cost of computing.…”

“…Note that d represents the dimensions x , y , or z . Due to the matrices independence from the image, they are generated, stored, and repeatedly used [ 45 , 68 ]. The process for the matrices generation is depicted in Figure 7 .…”

“…In particular, different methods are utilized to extract the features from the images. These methods can be classified as deep-learning-based methods, orthogonal-moment-based methods, and texture-based methods [ 41 , 42 , 43 , 44 , 45 ]. While the recognition accuracy of deep-learning-based methods can be very high, these methods run into a substantial amount of computational complexity, as explained in [ 46 , 47 , 48 ].…”

Plain, Edge, and Texture Detection Based on Orthogonal Moment

“…Transform-based techniques are used in various types of algorithms because of their powerful properties. Such algorithms are: speech enhancement [12], [13], video content analysis [14], face recognition [15], [16], and information hiding [17]. Primarily, DOMs are characterized by their energy compaction and localization properties [11].…”