Facial Feature Extraction Research Articles

Ekstraksi Fitur Wajah Menggunakan Metode Viola Jones dengan Tools Cascade Detector

The face is an important part of a person's characteristics that can be recognized by their differences from one another. In image processing, extracting features from faces is the most crucial thing, because features such as the eyes, nose, mouth and human face are very complex to be recognized by a computer. One of them is how the computer can recognize faces and see them like human eyes. Feature extraction is needed in image processing so that the computer can recognize and differentiate facial objects. This cascade is a method used to detect objects and the algorithm has a high degree of accuracy. This method is considered fast and robust for face detection. From the results of the implementation of facial feature extraction using Cascade Detector, it can be concluded that the feature extraction results for the detection of the face and nose produce an accuracy of 85.2% while for the detection of the right eye, left eye and mouth produce an accuracy of 81.9%, which means this level of accuracy smaller than face and nose detection. For the average accuracy value of each extraction of facial features, right eye, left eye, nose and mouth, that is equal to 83.22%. With an error of about 3% because there are detected animal faces. For Animal Nose 1% is detectable

Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP.

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.

Typical Facial Expression Network Using a Facial Feature Decoupler and Spatial-Temporal Learning

Facial expression recognition (FER) accuracy is often affected by an individuals unique facial characteristics. Recognition performance can be improved if the influence from these physical characteristics is minimized. Using video instead of single image for FER provides better results but requires extracting temporal features and the spatial structure of facial expressions in an integrated manner. We propose a new network called Typical Facial Expression Network (TFEN) to address both challenges. TFEN uses two deep two-dimensional (2D) convolutional neural networks (CNNs) to extract facial and expression features from input video. A facial feature decoupler decouples facial features from expression features to minimize the influence from inter-subject face variations. These networks combine with a 3D CNN and form a spatial-temporal learning network to jointly explore the spatial-temporal features in a video. A facial recognition network works as an adversarial network to refine the facial feature decoupler and the network performance by minimizing the residual influence of facial features after decoupling. The whole network is trained with an adversarial algorithm to improve FER performance. TFEN was evaluated on four popular dynamic FER datasets. Experimental results show TFEN achieves or outperforms the recognition accuracy of state-of-the-art approaches.

Algorithm Analysis of Face Recognition Robot Based on Deep Learning

With the continuous improvement of computer software and hardware performance, a large amount of image and video data can be easily collected and quickly transmitted, and new recognition methods that introduce deep learning are emerging, making the application and research of face recognition technology. The value is also increasingly prominent. The purpose of this paper is to study the face recognition robot implementation algorithm based on deep learning. The research background and significance of face recognition and expression recognition, which are the core of facial biological information extraction, are introduced. The face feature extraction network structure of Inception-ResNet-V1 has been improved, and high recognition features of faces can be obtained. At the same time, the training of the feature extraction model of the self-built training set and the adjustment of hyperparameters are completed. Finally, the effectiveness of the improved network in this paper is fully verified in the LFW test set and the actual robot environment. It is verified by experiments that the proposed optimization method can improve the performance of the network. It also verified the significant research significance of the current deep learning direction through practice.

Interactive Computer System Using Hand Gestures

Abstract: This paper proposes a way to control the position of cursor with the bare hands without using any electronic device. While the operations like clicking and dragging of objects will be performed with different hand gestures. Only authorized persons can access the system by using facial recognition. Face recognition tracks target objects in live video images taken with a video camera. This application is based on face detection, feature extraction and recognition algorithms, which automatically detects the human face when the person in front of the camera recognizing the person. If the person is authorized then he can access the computer. We used transformational algorithm, K-cosine algorithm in face detection and finger detection.

A facial geometry based detection model for face manipulation using CNN-LSTM architecture

This issue of DeepFake technique that may cause great threat to privacy, democracy, and national security has attracted the attention of deep learning researchers. DeepFake detection, therefore, has been a very hot issue in deep learning research. The face landmark feature maps are often used by many DeepFake approaches in generating fake faces. It also provides key information to help to detect manipulated face images. In this paper, we propose a detection approach for manipulated face images. To make full use of face landmark information, we propose a facial geometry prior module (FGPM) to extract facial geometry feature maps. Then the facial geometry feature maps are embedded into upsampling feature maps generated by the CNN-LSTM network. The proposed FGPM exploits facial maps and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating the CNN-LSTM network. Finally, a decoder is used to learn the mapping from low-resolution feature maps to pixel-wise to predict manipulation localization. Or a softmax classifier is used to predict real or fake face images. By experimenting on several popular datasets, the proposed detection model has demonstrated the capability of localizing manipulation at the pixel level and with a high prediction on real or fake face images.

UNSUPERVISED ADAPTATION OF A PERSON-SPECIFIC MANIFOLD OF FACIAL EXPRESSION

The project offers feature analysis and NN-classifiers-based speech emotion identification from voice data. By associating facial expressions with categories of fundamental emotions, automatic face emotion recognition (SER) has dominated psychology and plays a significant role in HCI systems for evaluating people's emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). Facial emotion detection, feature extraction and selection, and classification are all parts of the recognition system. These characteristics are helpful in properly differentiating the greatest number of samples, and the discriminant analysis-based NN classifier is utilised to categorise the six distinct phrases. The simulated results will demonstrate that, compared to previous Face emotion expression recognition systems, filter-based feature extraction with a utilised classifier provides substantially greater accuracy with less algorithmic complexity.

Real-time facial emotion recognition system among children with autism based on deep learning and IoT

Diagnosis of autism considers a challenging task for medical experts since the medical diagnosis mainly depends on the abnormalities in the brain functions that may not appear in the early stages of early onset of autism disorder. Facial expression can be an alternative and efficient solution for the early diagnosis of Autism. This is due to Autistic children usually having distinctive patterns which facilitate distinguishing them from normal children. Assistive technology has proven to be one of the most important innovations in helping people with autism improve their quality of life. A real-time emotion identification system for autistic youngsters was developed in this study. Face identification, facial feature extraction, and feature categorization are the three stages of emotion recognition. A total of six facial emotions are detected by the propound system: anger, fear, joy, natural, sadness, and surprise. This section proposes an enhanced deep learning (EDL) technique to classify the emotions using convolutional neural network. The proposed emotion detection framework takes the benefit from using fog and IoT to reduce the latency for real-time detection with fast response and to be a location awareness. From the results, EDL outperforms other techniques as it achieved 99.99% accuracy. EDL used GA to select the optimal hyperparameters for the CNN.

Person re-identification from multiple surveillance cameras combining face and body feature matching

In this work, a new method for human re-identification (Re-ID) from multiple surveillance cameras is proposed. Unlike traditional methods in which only the body features are used for matching, our proposed method uses both body and facial features for the Re-ID process. This combination allows us to re-identify people in challenging conditions, such as people with a uniform or similar-looking outfit, a partial or occluded body, appearance changes, or illumination. The face and body feature extraction models were developed using the state-of-the-art deep neural backbones and the synthesis of existing datasets in the literature. The performance of the method was evaluated on a self-generated dataset, which contains images under challenging conditions. The evaluation results show that our method outperforms traditional methods, in which the accuracy Rank1 reaches 91.30% while the traditional ones have a Rank1 of only 86.96%. This newly introduced method can be used for many practical applications in security surveillance of buildings and offices where challenging conditions often appear.

Facial Emotion Recognition through Neural Networks

Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.

GENDER RECOGNITION AND AGE DETECTION USING HUMAN FACIAL FEATURES

One of the active study fields in recent years is the extraction of information from the human face. Numerous investigations on the identification of the most prevalent face variety, including age and gender, have been done. The approach for automatically determining a person's age and gender from their face is suggested in this paper and is based on convolutional neural networks and support vector machines. The four steps of this method are face detection, pre-processing, feature extraction, and classification. A variety of convolutional neural networks have been trained to detect faces in live feed and also determine their age and gender are part of our system. Our model was trained using Adience benchmark dataset for age and gender prediction available on Kaggle, which has produced positive results. Keywords: Convolution neural network, Support Vector Machine, Face Detection, Multi-Task Cascaded Convolution Neural Networks, Principal Component Analysis, Adience benchmark dataset.

Cross-modal information fusion for voice spoofing detection

In recent years, speaker verification systems have been used in many production scenarios. Unfortunately, they are still very vulnerable to different kinds of spoofing attacks, such as speech synthesis attacks, replay attacks, etc. Researchers have proposed many methods to defend against these attacks, but in the existing methods, researchers just focus on speech features. In recent studies, researchers have found that speech contains a large amount of face information. In fact, we can determine the speaker’s gender, age, mouth shape, and other information by voice. These information can help us distinguish spoofing attacks. Inspired by this phenomenon, we propose a generalized framework named GACMNet. To cope with different attack scenarios, we instantiated two different models. Our framework is mainly divided into data pre-processing phase, feature extraction phase, feature fusion phase, and classification phase. Specifically, our framework consists of two branches. On the one hand, we extract face features in speech by a convolutional neural network. On the other hand, we use a densely connected network to extract speech features. For the more, we designed a global attention-based information fusion mechanism to distinguish the importance of each part of the features. Our solution was proven to be effective in two large scenarios. Compared to the existing methods, our model improves the tandem decision cost function (t-DCF) and equal error rate (EER) scores by 9% and 11% in the logical access scenario, respectively, our model improves the EER score by 10% in the physical access scenario.

Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.

Current artificial intelligence systems for determining a person's emotions rely heavily on lip and mouth movement and other facial features such as eyebrows, eyes, and the forehead. Furthermore, low-light images are typically classified incorrectly because of the dark region around the eyes and eyebrows. In this work, we propose a facial emotion recognition method for masked facial images using low-light image enhancement and feature analysis of the upper features of the face with a convolutional neural network. The proposed approach employs the AffectNet image dataset, which includes eight types of facial expressions and 420,299 images. Initially, the facial input image's lower parts are covered behind a synthetic mask. Boundary and regional representation methods are used to indicate the head and upper features of the face. Secondly, we effectively adopt a facial landmark detection method-based feature extraction strategy using the partially covered masked face's features. Finally, the features, the coordinates of the landmarks that have been identified, and the histograms of the oriented gradients are then incorporated into the classification procedure using a convolutional neural network. An experimental evaluation shows that the proposed method surpasses others by achieving an accuracy of 69.3% on the AffectNet dataset.

Missing Person Identification

At present the Computer automated Face recognition systems are used for personal identification, but the Age variations of an individual poses a serious problem for it. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. To classify person age using faces author using combination of two CNN where one CNN will extract face features which can help in identify changes in face over time and second CNN helps in predicting/ classifying age. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face features into two uncorrelated components—identity- and age-related features—through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. d. This system will decrease the crimes and ensure the security in our society.

A lightweight convolutional neural network built on inceptio-residual and reduction modules for deep facial recognition in realistic conditions

ABSTRACT Since the last two decades, face recognition has been a significant area in the fields of biometrics and machine learning that uses the different appearances and geometric-based facial features to recognize the living substances present in a frame or video. In this article, we have proposed an inceptio-residual convolutional neural network inspired by the ResNet architecture for deep facial feature extraction and recognition. Besides that, some of the well-known lite or moderately dense and highly accurate ImageNet benchmarked convolutional neural networks, such as VGG-19 BN, ResNet 18/34/50, SE-ResNet50, ResNeXt50, Inception V2/V3/V4, Xception50, and DualPathNet68, have been utilized for face recognition. In conjunction with that, the proposed one was tested along those above-standard networks with respect to various performance metrics. Based on the results, we concluded that the proposed Incepto-residual convolutional neural network has performed better than the ImageNet benchmarked Convolutional Neural Networks.

A Real-Time Face Detection Method Based on Blink Detection

Aiming at the photo fraud that often occurs in identity verification and the accuracy and robustness of real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. This method first extracts the image texture features through the LBP algorithm, which eliminates the problem of illumination changes to a certain extent. Then the extracted features are input into the ResNet network, and the facial feature extraction is enhanced by adding an attention mechanism is added to enhance the face feature extraction. Meanwhile, the BiLSTM method is used to extract the temporal characteristics of images from different angles or at different times to obtain more facial details. In addition, the fusion of local and global features is realized by SPP pooling, which enriches the expression ability of feature maps and improves detection accuracy. Finally, the eye EAR value is calculated by the face key point detection technology to achieve face anti-spoofing, and then the real-time face recognition against fraud is realized. The experimental results show that the algorithm proposed in this paper has good accuracy on NUAA, CASIA-SURF and CASIA-FASD datasets, which can reach 99.48%, 98.65% and 99.17%, respectively.

ARTIFICIAL INTELLIGENCE FACE RECOGNITION: SECURITY SYSTEM

Face recognition has recently been a popular study topic as a result of the growing demand for security. Security systems, identity verification, access control, surveillance systems, and social networks are just a few of the applications that can be expanded from facial recognition. However, as passwords and fingerprint scanners are supplanted by improved computer technology and more precise algorithms, facial recognition is becoming increasingly popular. Facial recognition technology is the simplest and most coherent of all the techniques. Face recognition does not necessitate an individual’s active participation. Face recognition systems are theoretically capable of minimizing risk and eventually avoiding future attacks in places where identity identification is necessary, such as airports and border crossings. If there are criminals on the loose, the same justification can be made as with surveillance devices. Surveillance cameras with face recognition capabilities will aid in the identification of these individuals. Alternatively, these surveillance systems can be used to track down the whereabouts of missing persons, although this is contingent on the use of powerful facial recognition algorithms and a fully built facial database. Face detection, facial feature extraction (image normalization), face identification, and results are the major operations to be conducted for face recognition.

Detection of Worker’s Safety Helmet and Mask and Identification of Worker Using Deeplearning

This paper proposes a method for detecting a helmet for the safety of workers from risk factors and a mask worn indoors and verifying a worker’s identity while wearing a helmet and mask for security. The proposed method consists of a part for detecting the worker’s helmet and mask and a part for verifying the worker’s identity. An algorithm for helmet and mask detection is generated by transfer learning of Yolov5’s s-model and m-model. Both models are trained by changing the learning rate, batch size, and epoch. The model with the best performance is selected as the model for detecting masks and helmets. At a learning rate of 0.001, a batch size of 32, and an epoch of 200, the s-model showed the best performance with a mAP of 0.954, and this was selected as an optimal model. The worker’s identification algorithm consists of a facial feature extraction part and a classifier part for the worker’s identification. The algorithm for facial feature extraction is generated by transfer learning of Facenet, and SVM is used as the classifier for identification. The proposed method makes trained models using two datasets, a masked face dataset with only a masked face, and a mixed face dataset with both a masked face and an unmasked face. And the model with the best performance among the trained models was selected as the optimal model for identification when using a mask. As a result of the experiment, the model by transfer learning of Facenet and SVM using a mixed face dataset showed the best performance. When the optimal model was tested with a mixed dataset, it showed an accuracy of 95.4%. Also, the proposed model was evaluated as data from 500 images of taking 10 people with a mobile phone. The results showed that the helmet and mask were detected well and identification was also good.

Revealing and Classification of Deepfakes Video's Images using a Customize Convolution Neural Network Model

Deepfake has been exploited in recent years despite its widespread usage in a variety of areas to create dangerous material such as fake movies, rumors, and false news by changing or substituting the face information of the sources and so poses enormous security concerns to society. Research on active detection & prevention technologies is critical as deepfake continues to evolve. Deepfake has been a blessing, but we've taken advantage of it by utilizing it to swap faces. Deepfake is a new subdomain of Artificial Intelligence (AI) technology in which one person's face is layered over another person's face, which is becoming more and more popular on social networking sites. Deepfake pictures and videos can now be created much more quickly and cheaply due to ML (Machine Learning), which is a primary component of deepfakes. Despite negative connotations attached to the term "deepfakes," technology is increasingly being used in commercial & individual contexts. New technical advancements have made it more difficult to distinguish between deepfakes and images that have been digitally manipulated. The rise of deepfake technologies has sparked a growing sense of unease. The primary goal of this project is to properly distinguish deepfake pictures from real images using deep learning techniques.In this study, we implemented a customized CNN algorithm to identify deepfake pictures from a video dataset and conducted a comparative analysis with two other methods to determine which way was superior. The Kaggle dataset was used to train & test our model. Convolutional neural networks (CNNs) have been used in this research to distinguish authentic & deepfake images by training three distinct CNN models. A customized CNN model, which includes several additional layers such as a dense layer, MaxPooling, as well as a dropout layer, has also been developed and implemented. This method follows the frames extraction, face feature extraction, data preprocessing, and classification phases in determining whether Real or Fake images in the video reflect the objectives. Accuracy, loss, and the area under the receiver operating characteristic (ROC) curve were used to characterize the data. Customized CNN outperformed all other models, achieving 91.4% accuracy, a reduced loss value of 0.342, as well as an AUC of 0.92. Besides, we obtained 85.2% testing accuracy from the CNN and 95.5% testing accuracy from the MLP-CNN model.

A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism.

Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long-distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and background environment, the face images captured by the device are still insufficient in the recognition rate of existing face recognition models. We propose an AB-FR model, a convolutional neural network face recognition method based on BiLSTM and attention mechanism. By adding an attention mechanism to the CNN model structure, the information from different channels is integrated to enhance the robustness of the network, thereby enhancing the extraction of facial features. Then, the BiLSTM method is used to extract the timing characteristics of different angles or different time photos of the same person so that convolutional blocks can obtain more face detail information. Finally, we used the cross-entropy loss function to optimize the model and realize the correct face recognition. The experimental results show that the improved network model indicates better identification performance and stronger robustness on some public datasets (such as CASIA-FaceV5, LFW, MTFL, CNBC, and ORL). Besides, the accuracy rate is 99.35%, 96.46%, 97.04%, 97.19%, and 96.79%, respectively.