Dynamic training using multistage clustering for face recognition

Salient feature and reliable classifier selection for facial expression classification

“…The performance of SFRCS is compared against both baseline and state-of-the-art contemporary methods that were also evaluated on the JAFFE database under the L-O-sample-O and/or L-O-subject-O evaluation strategies. The baseline algorithms that we implemented provide good classification solutions for overcoming the "small sample size" (SSS) problem, where the facial image sample dimensionality is larger than the number of available training samples per class [42,43]. As a result, the lack of sufficient training samples causes improper estimation of a linear separation hyper-plane between the classes.…”

K-Medoids Clustering Using Partitioning Around Medoids for Performing Face Recognition

“…Face recognition using Fuzzy c-Means clustering and sub-NNs was developed by Lu J, Yuan X and Yahagi T [15]. Clustering has also found its use in dynamic and 3D face recognition applications too [16] [17]. There are many other methodologies which have been proposed for efficient face recognition and are being improvised incessantly [18].…”

Symmetric Subspace Learning for Image Analysis

“…The most popular supervised SL technique is Linear Discriminant Analysis (LDA) [11]- [13], which seeks for a linear subspace, where the projected data classes are optimally discriminated. LDA-based techniques have been successfully used in many computer vision applications, such as human face recognition [14] and facial expression classification [15]. In [16], a discriminant NMF algorithm has been proposed, which achieves data decomposition, while increasing the between-class data dispersion and decreasing the within-class dispersion.…”