Gender recognition based on face image using reinforced local binary patterns

Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership

The initial phase in an automatic facial analysis is face detection, which has been concentrated in the course of recent decades. It is very challenging to recognize a face from an image due to illumination, variations in pose, occlusion, scaling and facial expression. In our work, we propose a methodology to detect a face with arbitrary pose variations. First, a Normalized Pixel Difference (NPD) feature is calculated from the face image. Second, the optimal subset of NPD features and their combinations are refined via Deep Quadratic Tree (DQT). In order to learn the NPD feature based deep quadratic trees, Discrete Adaboost classifier is adopted. The experimental results on Labeled Faces in the Wild (LFW) face dataset demonstrate that our framework performs better than the previous methods in detecting multi-view faces.

Gender recognition from facial images has become one of challenging research problem in computer vision, security, verbal-nonverbal communication and human computer interaction applications nowadays. Because facial images include many information such as gender, facial expressions, age, ethnic origin in computer-aided applications, the success rate of the gender recognition depends on quality of facial images. In this paper, it is proposed a new gender recognition method combining Speed Up Robust Features (SURF) based Bags of Visual Words (BoW) and Support Vector Machine (SVM) algorithm unlike previous work. The method is tested on realistic frontal, left and right face images from modern gender recognition FERET dataset with 3560 samples to see efficiency of the proposed method. Experimental results show that the proposed method can obtain better gender recognition performance on FERET database and the accuracy level of on left and right face images is a bit lower than the average accuracy level of frontal ones.

This paper presents a new method for face recognition that is based on concatenation of Gabor and VGGFace2 features. The proposed method is composed of three stages: a feature extraction, concatenation and a recognition stage. The feature extraction stage is implemented using the pre-trained model VGGFace2 and PCA is used to reduce the Gabor features, while the recognition stage is implemented using full connected neural networks. The proposed method was evaluated on the IJB-A dataset and Labeled Faces in the Wild (LFW) dataset. The results show that it is able to achieve recognition accuracy more than 99%.

The secondary sex characteristics in faces of people are quite different due to variations of age, sex hormone, race and dress-up style. It is a very challenging work to build a gender recognition model for all kinds of people. This paper proposes to train a gender recognition model based on the deep convolutional network on a complete dataset. Our newly built complete dataset contains as many common variations of face images as possible. Based on this complete dataset, we design a very deep convolutional network as our gender classifier. We achieve an accuracy of 98.67% on the most challenging public database, labelled faces in the wild (LFW) [1]· We collect 10000 images from Internet and build a new dataset — Chinese wild database. Our model achieves the accuracy of 97.51% This indicates our model is robust to racial variation. In the above two experiments, our model achieves the state-of-the-art performances in the wild.

In 2020, the COVID-19 pandemic spread globally, leading to countries imposing health restrictions on people, including wearing masks, to prevent the spread of the disease. Wearing a mask significantly decreases distinguishing ability due to its concealment of the main facial features. After the outbreak of the pandemic, the existing datasets became unsuitable because they did not contain images of people wearing masks. To address the shortage of large-scale masked faces datasets, a developed method was proposed to generate artificial masks and place them on the faces in the unmasked faces dataset to generate the masked faces dataset. Following the proposed method, masked faces are generated in two steps. First, the face is detected in the unmasked image, and then the detected face image is aligned. The second step is to overlay the mask on the cropped face images using the dlib-ml library. Depending on the proposed method, two datasets of masked faces called masked-dataset-1 and masked-dataset-2 were created. Promising results were obtained when they were evaluated using the Labeled Faces in the Wild (LFW) dataset, and two of the state-of-the-art facial recognition systems for evaluation are FaceNet and ArcFace, where the accuracy of using the two systems was 96.1 and 97, respectively with masked-dataset-1 and 87.6 and 88.9, respectively with masked-dataset-2.

Face recognition can be installed in a surveillance system so that it can be used for monitoring, tracking and access control. An excellent, intelligent surveillance system should be sensitive to the objects far away from the camera. Unfortunately, due to the long-distance, objects like human faces captured by the camera are too small to identify. As to enhance the subtle color differences in the face image, in this paper we first improve the resolution of the captured image using deep convolution neural networks (DCNNs). Then the efficient features are extracted and used to do classification. As for verifying the effectiveness of the proposed method, we used three databases including AR face database, Georgia Tech face database (GT) database, and Labelled Faces in the Wild (LFW) database, altogether, to conduct the training and testing. Compared to the existing approaches, experimental results show that the identification accuracy of the proposed method outperforms any existing approaches.

Recently, centre loss that aiming to assist Softmax loss with the objectives of both inter‐class dispension and intra‐class compactness simultaneously, has achieved remarkable performance on convolutional neural network‐based face recognition. However, its advantages highly rely on the centre feature assumption, which influences the capacity of the final obtained face features. Inspired by the centre loss approach, a novel Orientation Truncated Centre Learning is proposed, which takes advantage of an orientation truncated centre function to make the centre feature learning have more suitable orientation for deep face recognition. Three metrics are proposed to evaluate how discriminative are the distributions of the learned features for MNIST visualisation. Experimental results on several challenging benchmarks, including fine‐grained labelled faces in the wild (FGLFW), labelled faces in the wild (LFW), YouTube faces (YTF), and benchmark of large‐scale unconstrained face recognition (BLUFR), show that the proposed approach can easily generate more favourable results than several state‐of‐the‐art competitors.

Face swapping refers to the task of changing the appearance of a face appearing in an image by replacing it with the appearance of a face taken from another image, in an effort to produce an authentic-looking result. We describe a method for face swapping that does not require training on faces being swapped and can be easily applied even when face images are unconstrained and arbitrarily paired. Our method offers the following contributions: (a) Instead of tailoring systems for face segmentation, as others previously proposed, we show that a standard fully convolutional network (FCN) can achieve remarkably fast and accurate segmentation, provided that it is trained on a rich enough example set. For this purpose, we describe novel data collection and generation routines which provide challenging segmented face examples. (b) We use our segmentations for robust face swapping under unprecedented conditions, without requiring subject-specific data or training. (c) Unlike previous work, our swapping is robust enough to allow for extensive quantitative tests. To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure how intra- and inter-subject face swapping affect face recognition. We show that intra-subject swapped faces remain as recognizable as their sources, testifying to the effectiveness of our method. In line with established perceptual studies, we show that better face swapping produces less recognizable inter-subject results (see, e.g., Fig. 2.1). This is the first time this effect was quantitatively demonstrated by a machine vision method. Some of the material in this chapter previously appeared in [47].

In face recognition, face rotation alignment is an important part of the recognition process. In this paper, we present a hierarchical detector system using eye and eye-pair detectors combined with a geometrical method for calculating the in-plane angle of a face image. Two feature extraction methods, the restricted Boltzmann machine and the histogram of oriented gradients, are compared to extract feature vectors from a sliding window. Then a support vector machine is used to accurately localize the eyes. After the eye coordinates are obtained through our eye detector, the in-plane angle is estimated by calculating the arc-tangent of horizontal and vertical parts of the distance between left and right eye center points. By using this calculated in-plane angle, the face is subsequently rotationally aligned. We tested our approach on three different face datasets: IMM, Labeled Faces in the Wild (LFW) and FERET. Moreover, to compare the effect of rotational aligning on face recognition performance, we performed experiments using a face recognition method using rotationally aligned and non-aligned face images from the IMM dataset. The results show that our method calculates the in-plane rotation angle with high precision and this leads to a significant gain in face recognition performance.

Palmprint Identification Using Gabor and Wide Principal Line Features

The complexity and applicability of facial recognition in the growing domains of security and authentication, payment, advertising and health care have accelerated the research and development of this niche in the artificial intelligence (AI) field. A facial recognition system utilizes technologies to recognize a human face through mapping of distinct facial features from raw data such as photography and video to information of known faces stored in the database. Recognizing faces in real-world images is a challenging task. Generally, a facial recognition system involves multiple processes such as facial detection, feature extraction, face matching and confidence scoring in which various techniques and algorithms are available in the form of libraries and frameworks. In this paper, an investigation is conducted using two pipelines to identify the effectiveness of two prevalent open-source libraries, OpenCV and OpenFace, on real-time face recognition system. In the OpenCV pipeline, Viola –Jones algorithm is chosen for face detection, while feature extraction is conducted using local binary pattern histogram (LBPH). Face detection is performed using dlib, while feature extraction is conducted using deep learning algorithm in the OpenFace pipeline. Performance measures of both pipelines are evaluated using Labelled Faces in the Wild (LFW) as a benchmark data set. In addition, a locally populated data set consisting of images of personnel who are given access to the office door system is used to experiment on the applicability of these pipelines on real streaming data. OpenFace achieved an AUROC of 0.98, while OpenCV scored 0.94. Both experiments inferred that facial recognition system with OpenFace pipeline outperforms OpenCV in all aspects of performance measures. Further development demonstrates the successful implementation of OpenFace pipeline to a functional prototype in replacing the manual door access control.

Most existing state-of-the-art deep learning algorithms discover sophisticated representations in huge datasets using convolutional neural networks (CNNs) that mainly adopt backpropagation (BP) algorithm as the backbone for training the face recognition problems. However, since decades ago, BP has been debated for causing trivial issues such as iterative gradient-descent operation, slow convergence rate, local minima, intensive human intervention, exhaustive computation, time-consuming, and so on. On the other hand, a competitive machine learning algorithm called extreme learning machine (ELM) emerged with extreme fast implementation and simple in theory has overcome the challenges faced by BP. The ELM advocates the convergence of machine learning and biological learning for pervasive learning and intelligence and has been extensively researched in widespread applications. Nonetheless, till date, none of the work of ELM has proved its competency in tackling face verification problem. Hence, in this paper, we are going to probe for the first time the feasibility of ELM-based network in handling the face verification task. We devise and propose a novel and distinguished hybrid local receptive field-based extreme learning machine with DeepID (hereinafter denoted as H-ELM-LRF-DeepID), to discriminate face pairs. The experimental results on the YouTube face database, labeled faces in the wild (LFW), and CelebFaces datasets have shed light upon the feasibility and usefulness of the H-ELM-LRF-DeepID in the face verification task.

Face recognition remains a crucial issue in computer vision with various applications. This paper introduces an adapted method to tackle the challenges posed by mask-wearing during the COVID-19 pandemic. We propose modifications to the bag of convolutional neural networks (BoCNN) framework, which combines CNNs and the bag of words (BoW) approach. Our main contribution is customizing the BoCNN algorithm to identify faces with masks by focusing on the visible facial regions, particularly the eyes and eyebrows. Facial landmarks are detected, and the region of interest (ROI) is extracted using techniques such as Media Pipe. A pre-trained CNN is then applied to sections within the ROI, enabling robust feature extraction that captures intricate details such as lighting variations and facial expressions while reducing the impact of mask occlusions. The extracted features are pooled to create a comprehensive representation for recognition. Extensive experiments on the labeled faces in the wild (LFW) dataset, including masked face images, demonstrate the superiority of our adapted BoCNN approach over traditional BoW and deep feature extraction methods, especially accurately recognizing masked faces. Additionally, we assess the generalizability of our method across multiple datasets and discuss potential limitations and future research directions. The proposed BoCNN-based solution proves effective in recognizing masked faces, making it highly relevant for applications in security, human-computer interaction, and various other domains affected by the COVID-19 pandemic.

With the rise of crimes associated with Automated Teller Machines (ATMs), security reinforcement by surveillance techniques has been a hot topic on the security agenda. As a result, cameras are frequently installed with ATMs, so as to capture the facial images of users. The main objective is to support follow-up criminal investigations in the event of an incident. However, in the case of miss-use, the user’s face is often occluded. Therefore, face occlusion detection has become very important to prevent crimes connected with ATM usage. Traditional approaches to solving the problem typically comprise a succession of steps: localization, segmentation, feature extraction and recognition. This paper proposes an end-to-end facial occlusion detection framework, which is robust and effective by combining region proposal algorithm and Convolutional Neural Networks (CNN). The framework utilizes a coarse-to-fine strategy, which consists of two CNNs. The first CNN detects the head element within an upper body image while the second distinguishes which facial part is occluded from the head image. In comparison with previous approaches, the usage of CNN is optimal from a system point of view as the design is based on the end-to-end principle and the model operates directly on image pixels. For evaluation purposes, a face occlusion database consisting of over 50[Formula: see text]000 images, with annotated facial parts, was used. Experimental results revealed that the proposed framework is very effective. Using the bespoke face occlusion dataset, Aleix and Robert (AR) face dataset and the Labeled Face in the Wild (LFW) database, we achieved over 85.61%, 97.58% and 100% accuracies for head detection when the Intersection over Union-section (IoU) is larger than 0.5, and 94.55%, 98.58% and 95.41% accuracies for occlusion discrimination, respectively.