An approach for facial expression classification

Face recognition has received significant attention because of its numerous applications in access control, security, and surveillance system. In real-world scenarios, uncontrollable lighting conditions, a variety of posture and facial expressions, and noisy facial images can degrade the face recognition accuracy. Hence, the research based on the HOG algorithm and pre-processing implementation framework processing framework to improve face recognition accuracy is proposed. This proposal consists of four stages where the first stage is to build a dataset of 15 subjects and has five series of multi-poses of facial images. The second stage is focused on enhancing the pre-processing framework that consists of denoising colored, illumination normalization, and facial alignment algorithms. For the third stage, the HOG algorithm is utilized as a feature descriptor to detect the face. The fourth stage is implementing the deep convolution neural network to evaluate the accuracy of face recognition. From the observation, the improvement in the accuracy rate is up by 4.37% after the enhancement of the pre-processing framework.

In this study, we present the performance of Support Vector Machine (SVM), Convolution Neural Network (CNN), and Artificial Neural Network (ANN) with Bag of Words (BoW), Histogram of Oriented Gradients (HOG), and Image Pixels (IP) for face recognition. SVM, CNN, and ANN are machine learning approaches and has been used for pattern recognition, especially in face recognition technology. BoW, HOG, and IP are being used for image feature extraction. The testing has been conducted from publicly available AT&T face database. Every individual subject consists of 10 images with different facial expression, different illumination and the dimensions of the images is unified as 92-by-112 pixels with PGM formats. SVM achieved recognition accuracy of 97.00%, 96.00%, and 98.00% with subsequently BoW, HOG, and IP. CNN achieved recognition accuracy of 94.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. ANN achieved recognition accuracy of 96.00%, 99.00%, and 99.50% with subsequently BoW, HOG, and IP. The experimental results show that the IP with ANN approach clearly outperformed the others approaches.

Detecting human faces in low-resolution images is more difficult than high quality images because people appear smaller and facial features are not as clear as high resolution face images. Furthermore, the regions of interest are often impoverished or blurred due to the large distance between the camera and the objects which can decrease detection rate and increase false alarms. As a result, the performance of face detection (detection rate and the number of false positives) in low-resolution images can affect directly subsequent applications such as face recognition or face tracking. In this paper, a novel method, based on cascade Adaboost and Histogram of Oriented Gradients (HOG), is proposed to improve face detection performance in low resolution images, while most of researches have been done and tested on high quality images. The focus of this work is to improve the performance of face detection by increasing the detection rate and at the same time decreasing the number of false alarms. The concept behind the proposed combination is based on the a-priori rejection of false positives for a more accurate detection. In other words in order to increase human face detection performance, the first stage (cascade Adaboost) removes the majority of the false alarms while keeping the detection rate high, however many false alarms still exist in the final output. To remove existing false alarms, a stage (HOG+SVM) is added to the first stage to act as a verification module for more accurate detection. The method has been extensively tested on the Carnegie Melon University (CMU) database and the low-resolution images database. The results show better performance compared with existing techniques.

Principal curvatures based rotation invariant algorithms for efficient texture classification

Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l¹ -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.

Object detection in images is a computing demanding task which usually needs to deal with the detection of different classes of objects, and thus requiring variations and adaptations easily provided by software solutions. Object detection algorithms are being part of real-time smarter embedded systems, such as automotive, medical, robotics and security systems. In most embedded systems, efficient implementations of object oriented algorithms need to provide high performance, low power consumption, and programmability to allow greater development flexibility. The Histogram of Oriented Gradients (HOG) is one of the most widely used algorithms for object detection in images. In this paper, we show our work towards mapping the HOG algorithm to an FPGA-based system consisting of multiple Nios II softcore processors and bearing in mind high-performance and programmability issues. We show how to reduce 19x the algorithms execution time by source to source transformations and specially avoiding redundant processing. Furthermore, we show how the use of pipelining processing using three Nios II processors allows a speedup of 49x compared to the embedded baseline application.

The potential of facial and facial expression recognitions has gained increased interest in social interactions and biometric identification. Earlier facial identification methods suffer from drawbacks due to the lower identification accuracy under difficult lighting conditions. This paper presents two novel new descriptors called Histogram of Directional Gradient (HDG) and Histogram of Directional Gradient Generalized (HDGG) to extracting discriminant facial expression features for better classification accuracy with good efficiency than existing classifiers. The proposed descriptors are based on the directional local gradients combined with SVM (Support Vector Machine) linear classification. To build an efficient face and facial expression recognition, features with reduced dimension are used to boost the performance of the classification. Experiments are conducted on two public-domain datasets: JAFFE for facial expression recognition and YALE for face recognition. The experiment results show the best overall accuracy of 92.12% compared to other existing works. It demonstrates a fast execution time for face recognition ranging from 0.4 to 0.7 s in all evaluated databases.

In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.

In order to solve the problems of low average gradient and long recognition time in traditional facial expression recognition method, a multi-scale detail enhancement method for facial dynamic expression recognition of sprint athletes is proposed. A principal component analysis method was used to establish the facial expression feature subspace of sprinters, to project and reduce the dimension of the facial dynamic expression feature vector of sprinters, and to obtain the low frequency information and high frequency information of the facial image of sprinters by bilateral filtering. The multi-scale details of expression are enhanced by using side suppression network model and improving image S curve. The feature vector of facial dynamic expression is input into support vector machine to recognise the facial dynamic expression of sprinter. Experimental results show that the average value of annoying gradient is about 98 and the shortest time s. is about 1.9.

In the era of smart digital transformation, automatic face recognition is the way of identification and verification of a person in many applications of security and authentication. If the person's 2D still image or 3D video frame is taken under controlled lighting and frontal face poses, then today automatic face recognition is a solved problem with more than 98 % accuracy. Under ideal condition of images, automatic face recognition outperforms manual face recognition rate. But the problem of uncontrolled illumination, occlusion, tilting of faces, different expressions of faces, use of accessories and hair color, growth of facial hair, aging effect of a person and low-resolution images makes automatic face recognition underperform; it gets defeated by human. Unless this problem is solved on real time, automatic face recognition system cannot be trusted for crucial security applications like e-passport, fraud detection, counter terrorism and mug-shot verification etc. Extensive research is underway to improve this technology. Traditionally Harr cascade classifier methods, histogram of oriented gradients (HoG), principal component analysis (PCA), Eigen-faces, and support vector machine (SVM) classifier are used in face recognition. Now modern deep learning innovation has superseded those traditional machine learning techniques with more computing power in GPUs and TPUs to tackle all variations in input image quality and ever-growing face database. The goal of this paper is to report the journey of face recognition system from traditional to modern techniques to increase precision and recall of the system.

The histogram of oriented gradient(HOG) is a feature descriptor used for object detection in the computer vision and image processing, and it is widely used for pedestrian detection. The conspicuous image feature can improve the detective accuracy of the pedestrian detection. In order to improve the conspicuousness of extracted gradient, this paper modifies the gradient extraction operator based on the traditional HOG algorithm. By the tests of different operators, this paper chooses the Prewitt operator to extract the gradient information. Experimental results indicate that the mean and variance of extracted gradient are larger than the gradient of traditional HOG algorithm. The extracted gradient should be generated the conspicuous HOG feature that may improve the performance of pedestrian detection.

Facial expression methods play a vital role in human-computer interaction and other fields, but there are factors such as occlusion, illumination, and pose changes in wild facial recognition, as well as category imbalances between different datasets, that result in large variations in recognition rates and low accuracy rates for different categories of facial expression datasets. This study introduces RCL-Net, a method of recognizing wild facial expressions that is based on an attention mechanism and LBP feature fusion. The structure consists of two main branches, namely the ResNet-CBAM residual attention branch and the local binary feature (LBP) extraction branch (RCL-Net). First, by merging the residual network and hybrid attention mechanism, the residual attention network is presented to emphasize the local detail feature information of facial expressions; the significant characteristics of facial expressions are retrieved from both channel and spatial dimensions to build the residual attention classification model. Second, we present a locally improved residual network attention model. LBP features are introduced into the facial expression feature extraction stage in order to extract texture information on expression photographs in order to emphasize facial feature information and enhance the recognition accuracy of the model. Lastly, experimental validation is performed using the FER2013, FERPLUS, CK+, and RAF-DB datasets, and the experimental results demonstrate that the proposed method has superior generalization capability and robustness in the laboratory-controlled environment and field environment compared to the most recent experimental methods.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Face Recognition plays a very important role in numerous occasions based on visual security in recent days. The current methods of face recognition are to extract the different features of different faces to distinguish from others, so feature extraction has become a vital step in face recognition. For this reason, this paper presents a new fusion of local difference binary (LDB) and histogram of oriented gradients (HOG) for face recognition. We use LDB descriptor to extract the local pattern features of a face image. At the same time, the edge features of the original image are extracted by using HOG descriptor. The proposed new fusion of features improves the shortcomings of the low accuracy and avoids the problems that the dimension of the general fusion of features is too high. The experimental results on ORL and Yale face database verify the validity of the proposed fusion of features.