Facial Feature Extraction Method Research Articles

AI based Face Recognition Attendance System

The AI Based Face Recognition Attendance System represents an innovative solution at the intersection of artificial intelligence and attendance management. Leveraging Python open-source libraries such as OpenCV and NumPy, alongside machine learning techniques, the system aims to streamline the attendance tracking process. By employing face detection algorithms and feature extraction methods, the system identifies individuals, records their attendance, and updates the database in real-time. The system architecture incorporates components like a frontend web application, real-time prediction module, registration form module, reporting module, Redis database, Streamlit framework, Insightface library, and AWS deployment. Through continuous improvement and innovation, the system enhances user experience, accuracy, and efficiency in attendance management.

Automated facial characterization and image retrieval by convolutional neural networks.

Developing efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks. We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation. Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

Skinophile: The Art Of Skincare Selection With Haar Wavelet

In this study, a mobile application has been developed with the primary objective of classifying facial skin types to facilitate the selection of appropriate skincare products tailored to individual skin needs. The motivation behind this research stems from the widespread issue of individuals being unaware of their specific skin types, which often leads to suboptimal skincare routines and, in some cases, adverse effects such as skin dullness and damage. To address this challenge, a comprehensive problem-solving approach has been adopted. The methodology encompasses the utilization of the Haar Wavelets method for facial image feature extraction, complemented by the application of the Support Vector Machine (SVM) algorithm for precise skin type classification. Face Recognition technology has also been integrated to enhance the accuracy and reliability of the classification process.In terms of system development, an Agile methodology has been employed, facilitating swift and cost-effective project completion. This agile approach ensures the development process is efficient, reducing both time and financial resources.The mobile application developed for this purpose utilizes the Python programming language, specifically incorporating the PyWavelets library. The culmination of this research effort is a user-friendly mobile application that enables users to capture their facial images. Subsequently, the application employs advanced algorithms to identify their specific skin type, providing personalized recommendations for suitable skincare products and routines. This innovative solution aims to empower individuals with the knowledge and tools necessary to enhance the effectiveness of their skincare regimen, ultimately promoting healthier and more radiant skin.

Fractional-weighted entropy-based fuzzy G-2DLDA algorithm: a new facial feature extraction method

Fractional-weighted entropy-based fuzzy G-2DLDA algorithm: a new facial feature extraction method

FPGA Based Facial Recognition System

Introduction: Face recognition research is motivated not just by fundamental security concerns, but also by the fact that it is required in many practical applications where human identity is required. Rapid improvements in technology such as digital cameras, the internet, mobile devices, and technological demands on security have facilitated and encouraged face recognition as one of the key biometric technologies. Face detection, feature extraction, and classification methods for face identification utilizing hardware description language HDL implemented in a Field Programmable Gate Array are investigated in this paper (FPGA).The research goals include.
 Methods: The Viola-Jones algorithm for face detection was developed, followed by developing an algorithm for feature extraction using Artificial Neural Networks (ANN),and finally feature matching using Hamming Distance. The whole system was implemented on FPGA, using VHDL.
 Results: The system successfully identifies the eye region of the face from the image, and extracts the features of each image then perform matching. The program output or display a result of image matched or image not matched. The execution time of the overall system speeds up due to the parallel processing of FPGA.
 Conclusion: The program was able to distinguish between two images of people based on their eye image, as well as detect minor expressional changes in the test image. Designing the full algorithm using FPGA help in speeding up the execution time of the processes, which gives the opportunity to build the system to work in real time with low cost due to its flexibility.

Gabor Log-Euclidean Gaussian and its fusion with deep network based on self-attention for face recognition

In this work, we proposed a face feature extraction method by Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component Analysis (called LGLG-WPCA). The proposed method extracts raw features from the multivariate Gaussian in the transform domain of Gabor wavelet and uses WPCA to get robust features. Because the space of Gaussian is a Riemannian manifold, it is difficult to incorporate the learning mechanism into the model. To address this issue, Log-Euclidean approach is used to embed the multivariate Gaussian into the linear space, and then use WPCA to learn discriminative face features. LGLG-WPCA is good at extracting the detail features of face image. Furthermore, another outstanding advantage of LGLG is that its features can be effectively integrated with the high-level features of deep learning network for face recognition in more complex environments. We presented the feature fusing approaches for face recognition based on Self-attention Network (SAN) and achieved obvious performance improvement to the-state-of-the-art deep networks including SENet and FaceNet. Experiments show the proposed method is robust under adverse conditions such as varying poses, skin aging and uneven illumination, and it is suitable for face image under small-scale datasets in complex environments, such as network-based or video-based person searching or tracking.

An Approach of Detecting the Age of a Human by Extracting the Face Parts and Applying the Hierarchical Methods

One of the key challenges that the computer vision is facing is the age prediction. A well efficient CNN is selected for age prediction by performing various CNN operations by taking the categories as age 40 and above age 40. The selected CNN method obtained a training accuracy of 100% at more than 100 epochs. Hence, 100 epochs is considered for training. At this, the validation accuracy achieved is 84.9%. Three kinds of age phases with an age gap of 20,10 and 5 are used to predict the age. The normal method results in very less accuracy. Hence a hierarchical method is formulated. Under the hierarchical method, CNN is trained to estimate the age gaps in decreasing order. Hence not a single classifier, a group of classifiers are used for testing the image. From traditional method to hierarchical method, the 20 age gap accuracy increased from 27% to above 60%, ten age gap increased from 12% to above 35%, and five age gap increased from 5.5% to above 21%. To improve further, the features of the face parts are derived and combined which improves the efficiency compared to normal method, but not good accuracy as Hierarchical method. The combination of hierarchical method along with the face feature extraction method results in a considerable improvement in accuracy.

Face recognition system with feature normalization

ABSTRACT This study proposed an identity verification system that uses face recognition. The system features face detection as well as facial feature extraction and comparison methods. Early methods of face detection involved using specific approaches coupled with a classifier to extract features and detect faces. Although these methods can detect faces quickly, they generate a high false positive rate. Recent face detection methods based on a deep learning structure are extremely accurate but time-consuming. This study realized a face detection method based on the histogram of oriented gradient. The proposed method is not as accurate as deep learning; however, it is fast and can complete instant computing. Early methods of face recognition also involved using feature extraction methods coupled with a classifier to complete face recognition; however, these methods were not extremely accurate. The emergence of deep learning has facilitated greatly increasing the accuracy of face recognition. A deep learning-based method requires the entire deep learning structure to be retrained when a system needs to add a new user. This requirement is not feasible in actual applications. A researcher has therefore proposed the FaceNet method, which uses deep learning structure to extract eigenvectors and calculates the distance between eigenvectors as a measure of face similarity. Thus, the entire deep learning structure does not need to be retrained when a new user is added to the system. In this paper, FaceNet was used to extract the eigenvector of a face. However, the experiments of this study showed that facial features extracted using FaceNet are unevenly distributed in different dimensions, and using the calculated distance of the eigenvector as a measure of face similarity will yield inaccurate results. Therefore, this study proposed a facial feature normalization comparison method. The experimental results verified that the proposed method can achieve more than 98% accuracy and can be applied in practice.

Research on Face Image Digital Processing and Recognition Based on Data Dimensionality Reduction Algorithm.

Because face recognition is greatly affected by external environmental factors and the partial lack of face information challenges the robustness of face recognition algorithm, while the existing methods have poor robustness and low accuracy in face image recognition, this paper proposes a face image digital processing and recognition based on data dimensionality reduction algorithm. Based on the analysis of the existing data dimensionality reduction and face recognition methods, according to the face image input, feature composition, and external environmental factors, the face recognition and processing technology flow is given, and the face feature extraction method is proposed based on nonparametric subspace analysis (NSA). Finally, different methods are used to carry out comparative experiments in different face databases. The results show that the method proposed in this paper has a higher correct recognition rate than the existing methods and has an obvious effect on the XM2VTS face database. This method not only improves the shortcomings of existing methods in dealing with complex face images but also provides a certain reference for face image feature extraction and recognition in complex environment.

User Authentication System Based on Speech and Cascade Hybrid Facial Feature

With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system.

A Novel Facial Thermal Feature Extraction Method for Non-Contact Healthcare System

The non-contact healthcare system is a system that can avoid germ infection, and can also provide comfortable and convenient health care services for the caregivers. In the current thermal imaging research, local or small area images are used to represent the overall temperature information of the participants, but the temperature feature of the face should not only be used in a small part and ignore other parts. The facial thermal image can show clear temperature feature but is not conducive to meaningful feature extraction. Therefore, this research proposed a novel facial thermal image feature extraction method, which is used facial landmarks to detect and cut 12 blocks to establish a new feature matrix based on color mean values and standard deviation values. It can establish clear features on facial thermal images. The core part of the proposed healthcare system is the use of a deep learning framework, which is based on CAFFE under the DIGITS platform. The CAFFE runs the classic CNN, GoogLeNet. Based on the acquired images and new feature types, four models were trained, which were used for the raw RGB image, raw thermal image, RGB feature image, and thermal feature image. In the experiment, 800 images were used for training and validation, and 200 images were used for testing. An additional 40 images were used for random testing. The experimental results show that RGB images cannot be effectively used, thermal images can effectively predict the health status, and thermal feature images have the highest prediction accuracy.

Similar Face Recognition Using the IE-CNN Model

In the field of face recognition, similar face recognition is difficult due to the high degree of similarity of the face structure. The following two factors are needed to make progress in this field: (i) the availability of large scale similar face training datasets, and (ii) a fine-grained face recognition method. With the above factors fulfilled, we make two contributions. First, we show how a large scale similar face dataset (SFD) can be assembled by a combination of automation and human in the loop, and divide the dataset into five grades according to different degrees of similarity. Second, a new fine-grained face feature extraction method is proposed to solve this problem using the attention mechanism which combines the Internal Features and External Features. The Labeled Faces in the Wild (LFW) database, CASIA-WebFace and similar face dataset (SFD) were selected for experiments. It turns out that the true positive rate is improved by 1.94 - 5.66% and the recognition accuracy rate improved by 2.08 - 5.8% for the LFW and CASIA-WebFace database, respectively. Meanwhile for SFD, the recognition accuracy rate improved by 18.80 - 35.84%.

CLPNet: Cleft Lip and Palate Surgery Support With Deep Learning.

Cleft lip and palate is the most common congenital malformation in oral and maxillofacial region. As a kind of facial plastic surgery, the most important factor for the success of cleft lip and palate repair surgery is the design of surgical markers and incisions. However, general hospitals especially in rural areas lack dependable medical resources, which makes the effect of the surgery hard to guarantee. To solve this problem, we propose a novel robotic surgery assistant technology based on deep learning to help reduce the technical threshold and improve the overall effect of cleft lip and palate repair surgery. For the first time, a robust dataset of cleft lip and palate cases is established, which can be used to train the model to locate surgical markers and incisions. Secondly, we build a strong baseline on this dataset by using state-of-the-art Hourglass architecture and residual learning, with two neoteric block designs, one of which enables stronger capability of generalization, while the other greatly reduces the complexity of the model, thus making efficient application possible. Finally, by comparing with other facial feature extraction methods, our models achieve the best results on multiple metrics, showing their strong superiority and adaptability on this task.

A Study about Principle Component Analysis and Eigenface for Facial Extraction

Facial recognition is one of the most successful applications of image analysis and understanding. This paper presents a Principal Component Analysis (PCA) and eigenface method for facial feature extraction. Several performance metrics, i.e. accuracy, precision, and recall are taken into account as a baseline of experiment. Furthermore, two public data sets, namely SOF (Speech on faces) and MIT CBCL Facerec are incorporated in the experiment. Based on our experimental result, it can be revealed that PCA has performed well in terms of accuracy, precision, and recall metrics by 0.598, 0.63, and 0.598, respectively.

Local Appearance based Novel Facial Feature Extraction Method for Human Expression Recognition

Local Appearance based Novel Facial Feature Extraction Method for Human Expression Recognition

Face feature extraction and recognition via local binary pattern and two-dimensional locality preserving projection

In this paper, we propose a novel face feature extraction approach based on Local Binary Pattern (LBP) and Two Dimensional Locality Preserving Projections (2DLPP) to enhance the texture features and preserve the space structure properties of a face image. LBP is firstly used to remove the effect of illumination and noise, which would enhance the detailed texture characteristics of face images. Then 2DLPP is performed to extract some prominent features and decrease the image dimension with space structure information. The Nearest Neighborhood Classifier (NNC) is used to recognize a face image at the end. In addition, the rule for dimension selection is studied from the results of experiments about choosing an appropriate feature dimension by 2DLPP computation. The experimental results on the Yale, the extended Yale B and CMU PIE C09 benchmark datasets showed that the proposed face feature extraction and recognition method achieves a better performance in comparison with similar techniques, and the proposed dimension selection rule can give an appropriate feature dimension in 2DLPP.

Facial age feature extraction based on deep sparse representation

In recent years, the research on facial age estimation has attracted more and more interests from the scholars and researchers in the field of psychological, aesthetic, forensic science and computer vision. However, facial age estimation is a challenging work: face age does not suffer from only the influence of intrinsic factors (e.g., genes) but also external factors (e.g., living conditions), so that it is difficult to find accurate features which can describe the change of age. Therefore, this paper proposes a robust face feature extraction method based on dynamic deep sparse representation. Combined with the respective characteristics of Active Appearance Model (AAM), Local Binary Patterns (LBP), Gabor and Bio-Inspired Features (BIF), this method will give sufficient consideration to the thinking way of object recognition of the human, similarity of adjacent ages and the classification principle of signal sparse representation. In addition, in order to reduce the interference of the face identity factor, two factors analysis method is proposed to separate the face identity factor. The experimental results show that the feature extraction method proposed in this paper has strong discrimination and robustness, which outperforms the state-of-the-art age estimation approaches.

Feature Extraction Trends for Intelligent Facial Expression Recognition: A Survey

Human facial expression is important means of non-verbal communication and conveys a lot more information visually than vocally. In Human-machine interaction facial expression recognition plays a vital role. Still facial expression recognition through machines like computer is a difficult task. Face detection, feature extraction and expression classification are the three main stages in the process of Facial Expression Recognition (FER). This survey mainly covers the recent work on FER techniques. It especially focuses on the performance including efficiency and accuracy in face detection, feature extraction and classification methods.

The Excellent Properties of a Dense Grid-Based HOG Feature on Face Recognition Compared to Gabor and LBP

To effectively represent facial features in complex environments, a face recognition method based on dense grid histograms of oriented gradients (HOG) is proposed. First, the face image is divided by numerous dense grids from which the HOG features are extracted. Then, all the grid HOG feature vectors are composed to realize the feature expression of the whole face, and the nearest neighbor classifier is used for recognition. In the FERET face database with complex changes in illumination, time, and environment, we test the gamma illumination correction, the spatial gradient direction, the size of the block, the standardization, and the face image resolution to find and analyze the optimal HOG parameters for face recognition. Finally, we compare our dense grid HOG with the two famous local facial feature extraction methods: the Gabor wavelet and the local binary pattern (LBP) on face recognition. The experimental results show that the dense grid HOG method is more suitable for the variations in time and environment. The feature extraction times of the dense grid HOG and LBP are similar. However, the dense grid HOG method uses fewer dimensions to obtain a better recognition rate than the LBP. Moreover, the dense grid HOG feature extraction time greatly outperforms the Gabor wavelet feature.

Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition

Face image variations such as expression and pose radically increase the intra-class variations which affect the performance of feature extraction methods. It is desirable to extract more robust local discriminative features to effectively represent such face variations. This paper proposes a novel facial feature extraction method which utilizes interpolation-based directional wavelet transform (DIWT) and local binary patterns (LBP). An efficient direction assessment method based on quadtree partitioning is implemented to facilitate adaptive direction selection in the local regions from the face images to obtain DIWT sub-bands. LBP histogram features are extracted from selected top-level DIWT sub-bands to obtain local descriptive feature set. Experimental results are simulated on ORL database, GT database, and FEI database. A comparison with various contemporary methods which include holistic, local descriptors and LBP-based non-adaptive multiresolution analysis methods illustrate the efficacy of the proposed method.