Face Image Database Research Articles

Assessing Conscientiousness and Identify Leadership Quality Using Temporal Sequence Images

Human Facial expressions exhibits the inner personality. Evaluating the inner personality is performed through questionnaires during recruitment process. However, the evaluation through questionnaires performs less due to anxiety, and stress during interview and prediction of leadership quality becomes a challenging problem. To the above problem, Temporal sequence based SENet architecture (TSSA) is proposed for accurate evaluation of personality trait for employing the correct person for leadership position. Moreover, SENet is integration with modern architectures for performance evaluation. In Proposed TSSA, face book facial images of a particular person for a period of one month and face images collect from different social environments and forms the sequential facial image database are analysed for personality trait estimation. Now a days, Facebook plays a vital role, where people express their emotions by posting images and updating their profile pictures. In TSSA method, 50 Facebook temporal sequence of images of person with answered questionaries during the face image collection forms as a Temporal sequence image (TSI) database for prediction of the Big Five personality trait. In order to get precise prediction, we have analysed the face images that were posted in a period of one month and validated the result with the next month face images from face book. Face images for predicting the personality, where asked to fill the Questionnaires through Google Forms increase the accuracy in prediction. The TSSA prediction results are utilized for assessment of a person’s conscientiousness for leadership quality suitability. The study implements Deep Learning algorithm with SENet architecture and compares with traditional algorithms. From the validation results the proposed TSSA method performs 96% of accuracy in conscientiousness prediction.

Surveillance System with Face Recognition Using Hog

Abstract: Due to various suspicious activity, the surveillance system continues to emerge in the technical field. Everyday security threats can have a serious impact on people's day-today activity. In this area, numerous techniques have been developed, however some issues have not yet been overcome. The research described in this white paper offers more precise video surveillance with less processing complexity. The system's most crucial component includes location, recognition and facial recognition. The technology pulls highlighted facial information from a live environment or video dataset. The currently recorded video data will then be used to extract the face and background frames. The extracted facial picture data is then compared to the database's face image. A security alarm or signal is issued to prompt the security team to take action if no data is discovered that fits the already-existing data. The suggested solution outperforms current systems in terms of accuracy, efficiency, and cost.

Convolutional neural networks reveal differences in action units of facial expressions between face image databases developed in different countries.

Cultural similarities and differences in facial expressions have been a controversial issue in the field of facial communications. A key step in addressing the debate regarding the cultural dependency of emotional expression (and perception) is to characterize the visual features of specific facial expressions in individual cultures. Here we developed an image analysis framework for this purpose using convolutional neural networks (CNNs) that through training learned visual features critical for classification. We analyzed photographs of facial expressions derived from two databases, each developed in a different country (Sweden and Japan), in which corresponding emotion labels were available. While the CNNs reached high rates of correct results that were far above chance after training with each database, they showed many misclassifications when they analyzed faces from the database that was not used for training. These results suggest that facial features useful for classifying facial expressions differed between the databases. The selectivity of computational units in the CNNs to action units (AUs) of the face varied across the facial expressions. Importantly, the AU selectivity often differed drastically between the CNNs trained with the different databases. Similarity and dissimilarity of these tuning profiles partly explained the pattern of misclassifications, suggesting that the AUs are important for characterizing the facial features and differ between the two countries. The AU tuning profiles, especially those reduced by principal component analysis, are compact summaries useful for comparisons across different databases, and thus might advance our understanding of universality vs. specificity of facial expressions across cultures.

Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models

Many genetic syndromes are associated with distinctive facial features. Several computer-assisted methods have been proposed that make use of facial features for syndrome diagnosis. Training supervised classifiers, the most common approach for this purpose, requires large, comprehensive, and difficult to collect databases of syndromic facial images. In this work, we use unsupervised, normalizing flow-based manifold and density estimation models trained entirely on unaffected subjects to detect syndromic 3D faces as statistical outliers. Furthermore, we demonstrate a general, user-friendly, gradient-based interpretability mechanism that enables clinicians and patients to understand model inferences. 3D facial surface scans of 2471 unaffected subjects and 1629 syndromic subjects representing 262 different genetic syndromes were used to train and evaluate the models. The flow-based models outperformed unsupervised comparison methods, with the best model achieving an ROC-AUC of 86.3% on a challenging, age and sex diverse data set. In addition to highlighting the viability of outlier-based syndrome screening tools, our methods generalize and extend previously proposed outlier scores for 3D face-based syndrome detection, resulting in improved performance for unsupervised syndrome detection.

Facial Recognition Technology Code of Ethics

Facial recognition technology (FRT) is a tool used to identify individuals by scanning their face and comparing it to a database of facial images. In the last decade, the use of FRT has rapidly increased and is now used all around the world. This has resulted in many ethical issues caused by taking an individual’s images without consent to train FRT programs, FRT misidentifying individuals, and using FRT to classify individuals as part of specific groups based on appearance alone. Because of these issues, a new code of ethics is proposed to provide a guideline for the creation and use of FRT. The code of ethics will prevent images of individuals from being taken and used without their consent and prevent FRT from being used unethically.

Alat Pendeteksi Suhu Tubuh dan Wajah (Kebutuhan Bukti Kehadiran) Berbasis Data

This research leads to the manufacture of presence tools by implementing a face recognition and detection system, and is equipped with body temperature measurements. In this study, for face detection and recognition, the Local Binary Pattern Histogram (LBPH) Algorithm and Haar Cascade of Classifier were applied. The algorithm's working system performs face detection and recognition quickly with the use of a webcam. The scope of this research is to conduct database preparation, training, detection, recognition, and face identification. For measuring body temperature using the MLX90614 Infrared temperature sensor, this temperature sensor utilizes infrared radiation, this sensor can automatically measure energy in infrared radiation which will become a temperature scale. The testing process carried out in this study uses 100 databases of facial images that will be read during the detection process. From the test results, the face detection and recognition system will work optimally at a distance of 30-100 cm from the Web cam. while the body temperature measurement system will be more accurate at a distance of 5-15 cm from the temperature sensor MLX90614.

F $F$‐norm two‐dimensional linear discriminant analysis and its application on face recognition

Two-dimensional linear discriminant analysis (2DLDA) is a widely applied extension of LDA that can cope with matrix input samples directly. However, its construction is based on a squared F $F$ -norm which will lead to sensitivity to noise and outliers. In this paper, a square-free F $F$ -norm 2DLDA is proposed to improve the robustness of 2DLDA. By losing the squared operation, the proposed method weakens the influence of outliers and noise and at the same time keeps the geometric structure of data. It can be solved through an effective nongreedy iterative algorithm, with each subproblem having a closed-form solution. The algorithm is further proved to be convergent. Experiments on several human face image databases demonstrate the effectiveness and robustness of the proposed method.

Attendance Management System Using Facial Recognition

The main purpose of this project is to build a face recognition based attendance management system for any institution or organization to enhance and upgrade the current attendance monitoring system in to a more efficient and effective one. The current system has lot of ambiguity that causes inaccurate and inefficient attendance record. Problems of attendance monitoring and management that exists in the current system can be resolved using current emerging technologies. The technology working behind this proposed project will be the face recognition system. The human face is one of the natural traits that can uniquely identify an individual. Possibilities for a human face to change or being duplicated is low. In this project, databases of human face images will be created to use them in the recognizer algorithm. During the attendance human faces will be compared against the stored data to seek for identity. When an individual is identified, attendance will be recorded automatically by saving the necessary information in to an excel sheet database. Further the saved attendance information can be used for monitoring and management purposes.

MMPCANet: An Improved PCANet for Occluded Face Recognition

Principal Component Analysis Network (PCANet) is a lightweight deep learning network, which is fast and effective in face recognition. However, the accuracy of faces with occlusion does not meet the optimal requirement for two reasons: 1. PCANet needs to stretch the two-dimensional images into column vectors, which causes the loss of essential image spatial information; 2. When the training samples are few, the recognition accuracy of PCANet is low. To solve the above problems, this paper proposes a multi-scale and multi-layer feature fusion-based PCANet (MMPCANet) for occluded face recognition. Firstly, a channel-wise concatenation of the original image features and the output features of the first layer is conducted, and then the concatenated result is used as the input of the second layer; therefore, more image feature information is used. In addition, to avoid the loss of image spatial information, a spatial pyramid is used as the feature pooling layer of the network. Finally, the feature vector is sent to the random forest classifier for classification. The proposed algorithm is tested on several widely used facial image databases and compared with other similar algorithms. Our experimental results show that the proposed algorithm effectively improves the efficiency of the network training and the recognition accuracy of occluded faces under the same training and testing datasets. The average accuracies are 98.78% on CelebA, 97.58% on AR, and 97.15% on FERET.

Iranian emotional face database: Acquisition and validation of a stimulus set of basic facial expressions.

Facial expressions play an essential role in social interactions. Databases of face images have furnished theories of emotion perception, as well as having applications in other disciplines such as facial recognition technology. However, the faces of many ethnicities remain largely underrepresented in the existing face databases, which can impact the generalizability of the theories and technologies developed based on them. Here, we present the first survey-validated database of Iranian faces. It consists of 248 images from 40 Iranian individuals portraying six emotional expressions-anger, sadness, fear, disgust, happiness, and surprise-as well as the neutral state. The photos were taken in a studio setting, following the common scenarios of emotion induction, and controlling for conditions of lighting, camera setup, and the model's head posture. An evaluation survey confirmed high agreement between the models' intended expressions and the raters' perception of them. The database is freely available online for academic research purposes.

Biometric masterkeys

Biometric authentication is used to secure digital or physical access. Such an authentication system uses a biometric database, where data are sometimes protected by cancelable transformations. This paper introduces the notion of biometric masterkeys. A masterkey is a feature vector such that the corresponding template matches with a significant number of templates stored in a cancelable biometric database. Such a masterkey is directly researched from a cancelable biometric database, but we also investigate another scenario in which the masterkey is fixed before the creation of the cancelable biometric database, providing additional access rights in the system for the masterkey’s owner. Experimental results are realized on three biometric databases: the fingerprint database FVC, the face image database LFW and the ECG database PTB. It shows the effectiveness and the efficiency of such masterkeys in both scenarios. In particular, from any given feature vector, we are able to construct a cancelable biometric database, for which the biometric template matches with all the templates of the database.

Research on Fast Face Retrieval Optimization Algorithm Based on Fuzzy Clustering

Aiming at the problem of long retrieval time for massive face image databases under a given threshold, a fast retrieval algorithm for massive face images based on fuzzy clustering is proposed. The algorithm builds a deep convolutional neural network model. The model can be used to extract features from face photos to obtain a high-dimensional vector to represent the high-level semantic features of face photos. On this basis, the fuzzy clustering algorithm is used to perform fuzzy clustering on the feature vectors of the face database to construct a retrieval pedigree map. When the threshold is passed in for database retrieval of the target face photos, the pedigree map can be quickly retrieved. Experiments on the LFW face dataset and self-collected face dataset show that the model is better than the commonly used K-means model in face recognition accuracy, clustering effect, and retrieval speed and has certain commercial value.

Non-negative Matrix Factorization for Dimensionality Reduction

Abstract—What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of matrix factorization. We discuss the main optimization algorithms, used to solve the NMF problem, and their convergence. The paper also contains a comparative study between principal component analysis (PCA), independent component analysis (ICA), and NMF for dimensionality reduction using a face image database. Index Terms—NMF, PCA, ICA, dimensionality reduction.

Illumination Analysis of Deep Face Recognition

Illumination variation significantly affects the performance of face recognition algorithms. However, it is difficult to construct a database that meets the demand of illumination analysis with current face image database construction technology. The influence of illumination variation on deep face recognition based on 3D face models is investigated. First a new method is presented to produce face images under arbitrary illuminations by using a 3D face model based on face basis image representation theory proposed. Then the database used for illumination analysis is constructed. Afterwards, an optimal way is explored to select illuminations used for constructing a high quality face image database by analyzing the impacts of different illumination sampling strategies on the performances of face recognition models. With the advantage of accurate illumination labeling of virtual data, the performances of different illumination labeling methods on the training of face recognition network are tested on a multi-task learning framework. Through experiments on the effects of different lighting sampling schemes and labeling methods on the performances of face recognition models carried out on virtual data and real data, it is concluded that using an appropriate amount of lightings of basis images to build a database is an effective lighting sampling scheme, and accurate lighting annotation can help further improve the face recognition rate, the corresponding recognition models have face recognition accuracy rates of more than 98% on virtual and real monitoring datasets which contain face images captured under very challenging lighting conditions. Proposed work improve the performance of deep face recognition models, and facilitate the construction of high quality face image databases with various illuminations.

Face recognition system based on the multi-resolution singular value decomposition fusion technique

This study proposes a Fusion, Feature-Level, Face Recognition System (FFLFRS) that is based on the Multi-Resolution, Singular Value Decomposition (MSVD) fusion technique. Face recognition in the FFLFRS is achieved via four processes: face detection, feature extraction, feature fusion, and face classification. In this system, the most significant face features (that is, the eyes, nose, and mouth) are first detected. Then, local and global features are extracted by the Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction approaches. Afterwards, the extracted features are fused by the MSVD method and classified by the Artificial Neural Network (ANN). The proposed FFLFRS was verified on 10,000 face images drawn from the face images database of the Olivetti Research Laboratory (ORL). Face recognition performance of this system was contrasted with levels of performance of three state of the art, fusion-level, face recognition systems (FRSs) depending on the Frequency Partition (FP), Laplacian Pyramid (LP), and Covariance Intersection (CI) fusion methods. Ten-thousand images were employed to test the proposed model and assess its performance, which was evaluated in terms of changes in pose, illumination, and expression, besides low resolution and presence of occlusion. The face recognition results of the proposed FFLFRS are encouraging. This system proved to be effective in dealing with images having challenges to face recognition and it could achieve a recognition accuracy as high as 97.78%.

Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval

Purpose: Face recognition is a geometric space recording activity that allows it to be used to distinguish the features of a face. Therefore, facial recognition can be used to identify ID cards, ATM card PINs, search for one’s committed crimes, terrorists, and other criminals whose faces were not caught by Close-Circuit Television (CCTV). Based on the face image database and by applying the Content-Base Image Retrieval method (CBIR), committed crimes can be recognized on his face. Moreover, the image segmentation technique was carried out before CBIR was applied. This work tried to recognize an individual who committed crimes based on his or her face by using sketch facial images as a query. Methods: We used an image sketch as a querybecause CCTV could not have caught the face image. The research used no less than 1,000 facial images were carried out, both normal as well asabnormal faces (with obstacles). Findings:Experiments demonstrated good enough in terms of precision and recall, which are 0,8 and 0,3 respectively, which is better than at least two previous works.The work demonstrates a precision of 80% which means retrieval of effectiveness is good enough. The 75 queries were carried out in this work to compute the precision and recall of image retrieval. Novelty: Most face recognition researchers using CBIR employed an image as a query. Furthermore, previous work still rarely applied image segmentation as well as CBIR.

Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.

The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.

Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models.

Failure to identify difficult intubation is the leading cause of anesthesia-related death and morbidity. Despite preoperative airway assessment, 75-93% of difficult intubations are unanticipated, and airway examination methods underperform, with sensitivities of 20-62% and specificities of 82-97%. To overcome these impediments, we aim to develop a deep learning model to identify difficult to intubate patients using frontal face images. We proposed an ensemble of convolutional neural networks which leverages a database of celebrity facial images to learn robust features of multiple face regions. This ensemble extracts features from patient images (n=152) which are subsequently classified by a respective ensemble of attention-based multiple instance learning models. Through majority voting, a patient is classified as difficult or easy to intubate. Whereas two conventional bedside tests resulted in AUCs of 0.6042 and 0.4661, the proposed method resulted in an AUC of 0.7105 using a cohort of 76 difficult and 76 easy to intubate patients. Generic features yielded AUCs of 0.4654-0.6278. The proposed model can operate at high sensitivity and low specificity (0.9079 and 0.4474) or low sensitivity and high specificity (0.3684 and 0.9605). The proposed ensembled model outperforms conventional bedside tests and generic features. Side facial images may improve the performance of the proposed model. The proposed method significantly surpasses conventional bedside tests and deep learning methods. We expect our model will play an important role in developing deep learning methods where frontal face features play an important role.

Robust two-dimensional capped [formula omitted]-norm linear discriminant analysis with regularization and its applications on image recognition

Two-dimensional linear discriminant analysis (2DLDA) is an effective matrix-based supervised dimensionality reduction method that expresses 2D data directly. However, 2DLDA magnifies the influence of outliers and noise since the construction of 2DLDA is based on squared Frobenius norm. To overcome its sensitivity, this paper investigates a two-dimensional capped l2,1-norm linear discriminant analysis, called 2DCLDA. The application of capped l2,1-norm makes 2DCLDA identify outliers and suppress the effect of noise effectively. To avoid singularity and give a stable performance, a regularization term is also considered in the between-class scatter. 2DCLDA is solved through a series of generalized eigenvalue problems, with each subproblem can be deemed as a weighted 2DLDA with regularization. It is proved that the proposed iteration algorithm monotonously decreases the objective of 2DCLDA. At last, 2DCLDA with its related approaches on several face image databases is compared, and the experimental results demonstrate the superiority of 2DCLDA, especially for noise data.

Emotion recognition from facial images with simultaneous occlusion, pose and illumination variations using meta-learning

Automatic facial emotion recognition in real-world situations like partial occlusions, varying head poses and illumination conditions are challenging to the machine learning community. The main reason is the lack of sufficient samples with the aforementioned conditions in the baseline datasets which throws difficulty in training a well performing machine learning or deep learning model. To overcome this challenge, we have adopted the concept of meta-learning. Meta-learning using prototypical networks (metric-based meta-learning) has been proven to be well-fit for few-shot problems without severe overfitting. We leverage the quick adaptation power of prototypical networks for emotion recognition in the scarcity of such diverse samples. We have used CMU Multi-PIE dataset which contains images with partial occlusions, varying head-poses and illumination levels for training and evaluating the model. For testing the adaptability of the system to intra-class and inter-dataset variations, AffectNet face database images have been used. The proposed method is named as ERMOPI (Emotion Recognition using Meta-learning across Occlusion, Pose and Illumination) which performs emotion recognition from facial expressions using meta-learning approach for still images and it is robust to partial occlusions, varying head poses and illumination levels which is the novelty of this work. The key benefit is the usage of less number of training samples compared to the existing work in emotion recognition and achieved comparable results with the state-of-the-art approaches. The proposed method achieved 90% accuracy for CMU Multi-PIE database images and 68% accuracy for AffectNet database images.