Face detection perceives great importance in surveillance paradigm and security paradigm areas. Face recognition is the technique to identify a person identity after face detection. Extensive research has been done on these topics. Another important research problem is to detect concealed faces, especially in high-security places like airports or crowded places like concerts and shopping centres, for they may prevail security threat. Also, in order to help effectively in preventing the spread of Coronavirus, people should wear masks during the pandemic especially in the entrance to hospitals and medical facilities. Surveillance systems in medical facilities should issue warnings against unmasked people. This paper presents a novel technique for concealed face detection based on complexion detection to challenge a concealed face assumption. The proposed algorithm first determine of the existence of a human being in the surveillance scene. Head and shoulder contour will be detected. The face will be clustered to cluster patches. Then determination of presence or absent of human skin will be determined. We proposed a hybrid approach that combines normalized RGB (rgb) and the YCbCr space color. This technique is tested on two datasets; the first one contains 650 images of skin patches. The second dataset contains 800 face images. The algorithm achieves an average detection rate of 97.51% for concealed faces. Also, it achieved a run time comparable with existing state-of-the-art concealed face detection systems that run in real time.

/pdf/a-novel-technique-for-automated-concealed-face-detection-in-408p4rb19u.pdf

A novel technique for automated concealed face detection in surveillance videos.

Face detection by low-resolution image (LR) is one of the key aspects of Human-Computer Interaction(HCI). Due to the LR image, which has changes in pose, lighting, and illumination, the performance of face recognition is reduced. In this work, we propose the Deep Belief Network-Crossover based Firefly (DBN-CROFF) method for face recognition from low-resolution images. The Histogram of Gradient (HOG) and 2-Dimensional Discrete Wavelet Transform (2D-DWT) to extract facial width, size of the cheeks, skin tone, nose, and lip shape features from facial data. The Kernel Principle Component Analysis (k-PCA) is used to successfully reduce the dimension of the feature. The experimental performance of the proposed method is evaluated using four datasets namely LFW, Multi-PIE, Extended Yale-B, and FERET with conventional techniques. Finally, the proposed DBN-CROFF solution surpasses the other conventional facial recognition approaches by giving a higher accuracy of recognization.

Improved crossover firefly algorithm based deep Beleif network for low-resolution face recognition

Face Recognition (FR) has been vastly studied in the past few decades and researchers have been able to achieve success in this field, mostly in controlled environments However, the FR algorithm faces huge drop in its accuracy due to the variation in pose and expression in face images This paper proposes a novel FR method to overcome the issues of pose and expression The proposed FR algorithm initially locates faces using dual shot face detection algorithm Later, an intelligent combination of Linear Discriminant Analysis (LDA) and Adaptive Boosting is used to extract features from detected and normalized face images Finally, classic nearest center classifier is applied to achieve classification Detailed experiments are conducted on JAFFE and KDEF databases Moreover, a thorough analysis and comparison of the effect of pose and expression variation on the accuracy of FR is presented The experimental results show that the proposed algorithm achieves better recognition accuracy than the recent FR methods

A Robust Face Recognition Method for Expression and Pose Variant Images

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.

/pdf/multiscale-retinex-application-to-analyze-face-recognition-4141ky1zy8.pdf

Multiscale Retinex Application to Analyze Face Recognition

Computer vision is currently playing an increasingly important role in automatically identifying the character of the image processing technology as research hotbed in the field of smart computing, OCR, face recognition, fingerprinting, biometric recognition, and so forth. Content-based image recovery, video recovery, multimedia collection, watermarking, games, film stunts, virtual reality, e-commerce, and other apps are available all round. The color pictures of parts taken by industrial cameras depend on computer performance and the intricate environment, and in particular, on the whole resolution image display, a lot of CPU resources are needed. Some details cannot be shown completely at the same time. If the image is not sufficiently clearly visible, methods for image processing like improvement, noise reduction, and interpolation must be used to improve color photo clarity. This article, based on the OpenCV platform, uses frequency domain filters, median filters, Fourier transform, and other image improvement technologies to remove image noise in order to enhance the quality of local photos from industrial cameras’ components. Finally, clear and available image information is obtained in different experimental methods, which check the application of image enhancement technology to image rebuilding. Finally, the performance of the proposed method in terms of CPBD value, definition value, and operation time is compared, which shows that the proposed method has obvious advantages in the above performance.

/pdf/partial-color-photo-processing-method-for-components-based-7o01gb8tce.pdf

Partial Color Photo Processing Method for Components Based on Image Enhancement Technology

Image enhancement algorithms are developed to achieve improved image quality. This paper presents a novel technique to enhance colored images using Multi Scale Retinex (MSR) along with the Guided Filter (GF). The proposed technique has several steps. Initially, the PCA isolates the input RGB image into the luminance and chrominance channels. On luminance channel, the MSR is applied in three steps. Meanwhile, the GF is introduced, which divides the image into high and low frequency components. Finally, Contrast Stretching (CS) is applied that expands the contrast of the image. Proposed method yields visually pleasing images on NASA retinex dataset and outperforms few techniques in terms of the entropy, the PSNR, and memory usage. In addition, the proposed method is computationally efficient.

Color Image Enhancement Using Multiscale Retinex with Guided Filter

Face images that appear in multimedia applications, such as digital entertainments usually exhibit dramatic nonuniform illumination, occlusions, low-resolution, and pose/expression variations that result in substantial performance degradation for traditional face recognition algorithms. Recent research is focused to develop robust face recognition algorithms to solve the aforementioned issues with maximum effort to mimic the human vision system. This paper presents a near real-time and novel face recognition method to recognize the occluded and low-resolution face images. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image. Meanwhile, the adaptive boosting and Linear Discriminant Analysis (LDA) are used to extract face features. In the final stage, classic nearest centre classifier is used for face classification. Detailed experiments are performed on two publicly available LFW and the AR databases. Simulation results reveal that the proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate.

Hidayat Ullah

Papers

Color Image Enhancement Using Multiscale Retinex with Guided Filter

A Robust Face Recognition Method for Occluded and Low-Resolution Images