The Real Time Face Recognition

Systematic Literature Review on the Accuracy of Face Recognition Algorithms

“…Ref. [20] (item 11 of Table 2) studied face recognition in video streams, typical of security and access control systems. For that, a system was developed based on the Viola-Jones algorithm, used to detect people in a sequence of video images and local binary templates using the Python programming language to classify the detected people, and an Open-Source Computer Vision library (OpenCV).…”

“…The second research question developed for this SLR addresses the accuracy rates identified in algorithms for real-time facial recognition, which is highlighted below, followed by its considerations. [10] Automatic attendance monitoring system using facial recognition through feature-based methods (PCA, LDA) HR LR LR [11] Automated attendance system using image processing HR LR LR [12] Computer vision on identifying persons under real time surveillance using IOT HR HR HR [13] Face detection and recognition-based e-learning for students' authentication: study literature review HR HR LR [14] Face recognition-based attendance system using machine learning algorithms HR HR LR [15] Human identification recognition in surveillance videos HR HR LR [16] Improving the capability of real-time face masked recognition using cosine distance HR HR LR [17] LBPH based improved face recognition at low resolution HR LR LR [18] Real-time face recognition: A survey HR HR LR [19] Recognizing Very Small Face Images Using Convolution Neural Networks HR HR LR [20] The Real Time Face Recognition HR LR MR [21] Fractional Krill-Lion algorithm-based actor critic neural network for facer recognition in real time surveillance videos HR HR LR [22] Technology: Person Identification MR MR HR…”

Automatic Face Mask Detector menggunakan Algoritma Viola and Jones

“…Cukup menggunakan dataset wajah, hidung dan mulut. Bahkan cascade detector dalam bentuk XML-nya sudah tersedia di internet [15].…”

Applying Gamma and Histogram Equalization Algorithms for Improving System-performance of Face Recognition-based CNN

“…It consists of four main stages which are: Integral Image, Haar Features, Haar Feature Classifier, and Cascade [8]- [10]. One of the most important problems that strongly affect the accuracy of the results of the viola algorithm is a low-quality image, especially in terms of lighting, if the image is very bright or dark [11], [12]. It has been used by many researchers in their studies, for example: In [13], Viola-Jones used it as the first stage of research in detecting the face and then recognizing faces using eigenface with an accuracy of 90%, the researcher also mentioned that the results are greatly affected by the conditions in which the image was taken, especially if the images were dark.…”

“…In [20], the Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE) was used to improve the images after detection of the faces in the image using the Viola-Jones, then three different types of CNN architectures were used, namely VGG16, ResNet50, and Inception-v3 to recognize the faces, and it achieved an accuracy of 99, 44% and 99, 89%. The problem of the last two researchers is that they did not address the problem of low-quality images in the first stage before detecting the face using the Viola-Jones, as the accuracy of the viola-Jones results is greatly affected by the quality of the images [21], [11], thus affects the accuracy of CNN's results in recognizing faces. Therefore, we proposed a system that addresses the problems of low-quality images in the first stage of the system using both gamma, and HE.…”