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.

面部是人类用于识别的最重要的生物特征之一。人脸识别是用于人类身份验证的重要生物识别方法，与指纹或掌纹扫描等其他生物识别系统相比，由于其独特的功能、非侵入性和便利性，它被应用于多个领域。尽管人脸识别领域已经取得了显着进步，但仍然存在一些问题，导致准确性无法超越人类。这项研究提出了一种新颖有效的框架，名为“利用蜘蛛层次结构和经典分类器进行面部识别”（FISH-CC），旨在识别人的面部、性别和年龄。该框架采用了一种基于合作博弈论（CGT）的新颖的面部边界定位方案，通过准确检测面部轮廓来增强面部检测性能。使用修改后的局部二进制模式 (mLBP) 从检测到的面部中提取特征。为了优化特征选择，实施了一种基于 CGT 的算法，称为具有前向特征选择 (FFS) 的扩展贡献选择算法 (ECSA)。最后，蜘蛛层次结构（SH）与经典分类器（CC）集成用于人脸识别。为了评估所提出方法的有效性，进行了许多测试，并利用野外标记面孔（LFW）数据库来验证性能。这项研究的结果表明，与现有方法相比，所提出的 FISH-CC 的准确率高达 99.60%。