Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing selected Haar-like and local binary pattern features, based on their number of uses at each stage of training using MATLAB’s trainCascadeObjectDetector function. We used 2577 positive face samples and 37,206 negative samples to train Haar-like and LBP face detectors for a range of False Alarm Rate (FAR) values (i.e., 0.01, 0.05, and 0.1). However, the study shows that the Haar cascade face detector at a low stage (i.e., at six stages) for 0.1 FAR value is the most efficient when tested on a set of classroom images dataset with 100% True Positive Rate (TPR) face detection accuracy. Though, deep learning ResNet101 and ResNet50 outperformed the average performance of Haar cascade by 9.09% and 0.76% based on TPR, respectively. The simplicity and relatively low computational time used by our approach (i.e., 1.09 s) gives it an edge over deep learning (139.5 s), in online classroom applications. The TPR of the proposed algorithm is 92.71% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 98.55% for images in MUCT face dataset “a”, resulting in a little improvement in average TPR over the conventional face identification system.

The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

Inspired by the human visual system to concentrate on the important region of a scene, attention modules recalibrate the weights of either the channel features alone or along with spatial features to prioritize informative regions while suppressing unimportant information. However, the floating-point operations (FLOPs) and parameter counts are considerably high when one is incorporating these modules, especially for those with both channel and spatial attentions in a baseline model. Despite the success of attention modules in general ImageNet classification tasks, emphasis should be given to incorporating these modules in face recognition tasks. Hence, a novel attention mechanism with three parallel branches known as the Low-Complexity Attention Module (LCAM) is proposed. Note that there is only one convolution operation for each branch. Therefore, the LCAM is lightweight, yet it is still able to achieve a better performance. Experiments from face verification tasks indicate that LCAM achieves similar or even better results compared with those of previous modules that incorporate both channel and spatial attentions. Moreover, compared to the baseline model with no attention modules, LCAM achieves performance values of 0.84% on ConvFaceNeXt, 1.15% on MobileFaceNet, and 0.86% on ProxylessFaceNAS with respect to the average accuracy of seven image-based face recognition datasets.

The current lightweight face recognition models need improvement in terms of floating point operations (FLOPs), parameters, and model size. Motivated by ConvNeXt and MobileFaceNet, a family of lightweight face recognition models known as ConvFaceNeXt is introduced to overcome the shortcomings listed above. ConvFaceNeXt has three main parts, which are the stem, bottleneck, and embedding partitions. Unlike ConvNeXt, which applies the revamped inverted bottleneck dubbed the ConvNeXt block in a large ResNet-50 model, the ConvFaceNeXt family is designed as lightweight models. The enhanced ConvNeXt (ECN) block is proposed as the main building block for ConvFaceNeXt. The ECN block contributes significantly to lowering the FLOP count. In addition to the typical downsampling approach using convolution with a kernel size of three, a patchify strategy utilizing a kernel size of two is also implemented as an alternative for the ConvFaceNeXt family. The purpose of adopting the patchify strategy is to reduce the computational complexity further. Moreover, blocks with the same output dimension in the bottleneck partition are added together for better feature correlation. Based on the experimental results, the proposed ConvFaceNeXt model achieves competitive or even better results when compared with previous lightweight face recognition models, on top of a significantly lower FLOP count, parameters, and model size.