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Top1. Introduction
Face recognition is an emerging technology in Artificial Intelligence and computer vision with many research ongoing to develop fast, robust, and more accurate system which can be used in many fields of application like security systems (Othman & Aydin, 2018), video surveillance or verification like done on various online platforms like Facebook and google photos. Apart from this face recognition is being applied in various real-life applications. Various algorithms for 2D and 3D face recognition are being developed (Abate et al., 2007; Masupha et al., 2015). Face recognition can be implemented in various real-life application like security and surveillance (Jaiswal et al., 2020), criminal identification, forensic applications (Kute et al.,2019), threat detection(Sarma et al., 2017), face verification, and attendance systems (Arsenovic et al., 2017).
1.1 Motivation
The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. Nowadays, various online platforms use their trade secret algorithms for face recognition and charge for them. The motivation of this paper is to develop a robust, fast, and optimized system for face recognition with the help of various freely available tools. The system developed thus can be implemented in any existing frameworks which can utilize the objective of face recognition.
1.2 Contribution
We devised a framework using the machine learning library Dlib for face detection and landmark estimation, a Residual Network for feature vector calculation, PCA for dimensional reduction, and various multi-class classifiers for classification. The experiment is done to reduce the computational times of various classifiers after applying PCA and fine-tuning of parameters of the classifiers. The performance of various classifiers is evaluated on the validation set which contains nonoverlapping data from the training dataset.
1.3 Organization
The rest of this paper is organized as follows. Section 2 comprises the related research in the field of learning-based facial recognition. The methodology of the proposed work is discussed in section 3. Section 4 describes the proposed algorithms and implementation is discussed in section 5. Section 6 deals with the experimental results and discussion thereon. Finally, section 7 provides conclusions with some future directions.
The research area of face recognition continues to evolve these days. It became an important part of various real-life applications. Several face recognition systems have been developed which are fast and robust. This section presents the background and a brief survey in this field.
2.1 Basic Principles in Facial Recognition
Face recognition algorithms are generally based on the feature extraction that is present in images of faces. The facial recognition system will compare the features present in the database with the features extracted from the face images. By defining some confidence value, when this confidence threshold value is crossed then the faces can be said to be similar, the result is then shown. The process of facial recognition can be divided into two categories: One-to-One comparison of image for confirmation; other is One-to-Many comparison for the sake of identification.
In a specialized way, facial recognition systems include a list of interdisciplinary technology to build a system, which includes the acquisition of face images, locating the faces, preprocessing for facial recognition like pose normalization (Al-Obaydy & Suandi, 2020), extraction of features, identity recognition, identity search, etc. In a generalized way, facial recognition is a system for identification either through search or comparison. Apart from this face recognition can be implemented into expression analysis like fatigue detection(Li et al.,2021).