Facial recognition is a popular biometric technique to recognize an individual by comparing the facial features of a given photograph or a sketch to the digitally stored photographs. One of the important applications of facial recognition is to determine the identity of criminals through their hand-drawn or composite sketches. Despite of the development made in sketch-based facial recognition, available approaches are facing various challenges. The component-based approach (CBA) measures the similarity between each facial component of a sketch and a mugshot photograph. The major challenge in this approach is to determine which facial components are crucial in the identification process. Certain facial components provide better recognition clue than others while matching with mugshot photographs and considerably accurate identification results could be achieved to incorporate such crucial components in the recognition process. In this article, we propose a novel methodology which is based on computable weights to find the most discriminative facial components by using the Weighted Component-Based Approach (WCBA). The weight vector is used during the similarity score measurement to enhance the accuracy and performance of the facial recognition system. Experimental results on matching 50 facial images from 1193 subjects of Multiple Encounter Dataset II (MEDS-II) and 85 facial images from CHUK face sketch database (CUFS) show that the proposed method achieves promising performance (accuracies of 58.33% and 88.23%, respectively) as compared to other leading facial recognition techniques (accuracies of 52% and 80%). We believe our prototype approach will be of great value to law enforcement agencies in the apprehension of culprits in a timely fashion.

面部识别是一种流行的生物识别技术，通过将给定照片或素描的面部特征与数字存储的照片进行比较来识别个人。面部识别的重要应用之一是通过其手绘或合成草图来确定罪犯的身份。尽管基于草图的面部识别技术得到了发展，但可用的方法仍面临着各种挑战。基于组件的方法（CBA）测量草图的每个面部组件与面部照片之间的相似度。这种方法的主要挑战是确定哪些面部成分在识别过程中至关重要。在与面部照片匹配的同时，某些面部组件比其他面部组件提供了更好的识别线索，并且可以实现相当准确的识别结果，以便将这些关键组件纳入识别过程。在本文中，我们提出了一种新颖的方法，该方法基于可计算的权重，通过使用基于加权分量的方法（WCBA）来找到最具区分性的面部分量。在相似度分数测量期间使用权重向量以增强面部识别系统的准确性和性能。通过匹配来自1193个多重遭遇数据集II（MEDS-II）的50个面部图像和来自CHUK面部素描数据库（CUFS）的85个面部图像进行匹配的实验结果表明，该方法取得了不错的效果（准确度为58.33％和88.23％，与其他领先的面部识别技术相比（准确度分别为52％和80％）。我们相信，我们的原型方法对于及时逮捕犯罪分子的执法机构将具有重要价值。