FISH-CC: novel face identification using spider hierarchy (FISH) with a classic classifier

Face is one of the most important biometric traits utilized by humans for recognition. Face recognition is the prominent biometric method for human authentication, and it is used in several domains due to its unique features, non-intrusive, and convenience compared to other biometric systems like fingerprint or palmprint scans. Although the field of face recognition has advanced significantly, there are still problems that prevent accuracy from surpassing that of humans. This study proposes a novel and effective framework, named Face Identification utilizing Spider Hierarchy with a Classic Classifier (FISH-CC), aimed at recognizing a person’s face, gender, and age. This framework incorporates a novel face boundary localization scheme based on cooperative game theory (CGT), enhancing facial detection performance by accurately detecting facial contour. Features are extracted from the detected faces using a modified local binary pattern (mLBP). To optimize feature selection, a CGT-based algorithm, known as the extended contribution selection algorithm (ECSA) with forward feature selection (FFS), is implemented. Finally, Spider Hierarchy (SH) integrated with a Classic Classifier (CC) is used for face identification. To assess the effectiveness of the proposed method, a number of tests are carried out, and the labeled faces in the wild (LFW) database are utilized to validate the performance. The outcomes of this study demonstrated that the proposed FISH-CC achieves a superior accuracy rate of 99.60% when compared to the existing approaches.