This paper presents a new proposal of an efficient computational model of face recognition which uses cues from the distributed face recognition mechanism of the brain, and by gathering engineering equivalent of these cues from existing literature. Three distinct and widely used features: Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Principal components (PCs) extracted from target images are used in a manner which is simple, and yet effective. The HOG and LBP features further undergo principal component analysis for dimensionality reduction. Our model uses multi-layer perceptrons (MLP) to classify these three features and fuse them at the decision level using sum rule. A computational theory is first developed by using concepts from the information processing mechanism of the brain. Extensive experiments are carried out using ten publicly available datasets to validate our proposed model's performance in recognizing faces with extreme variation of illumination, pose angle, expression, and background. Results obtained are extremely promising when compared with other face recognition algorithms including CNN and deep learning-based methods. This highlights that simple computational processes, if clubbed properly, can produce competing performance with best algorithms.
翻译:本文提出了一个关于高效的面部识别计算模型的新建议,该模型使用分布式大脑面部识别机制的提示,以及从现有文献中收集这些提示的工程等量。三种不同和广泛使用的特征:定向渐变结构(HOG)、局部二进制模式(LBP)和从目标图像中提取的主要组成部分(PC)的直图以简单而有效的方式使用。HOG和LBP特征还进行了减少维度的主要组成部分分析。我们的模型使用多层感应器(MLP)对这三个特征进行分类,并用总则将其结合到决策一级。计算理论首先通过使用大脑信息处理机制的概念加以发展。使用10个公开的数据集进行了广泛的实验,以验证我们拟议的模型在认识面部与极异的光化、角度、表达和背景的面貌方面的表现。获得的结果与其他面部识别算法(包括CNN和深层次的学习方法)相比是极有希望的。这突出表明,简单的计算过程,如果是俱乐部能够产生与最佳算法的竞争。