Design of Boosted Cascade and RTLBP Features based Face Recognition Module Using ML in OpenCV

Face detection and picture or video recognition is a popular subject of research on biometrics. Face recognition in a real-time setting has an exciting area and a rapidly growing challenge. Framework for the use of face recognition application authentication. This proposes the PCA (Principal Component Analysis) facial recognition system. The key component analysis (PCA) is a statistical method under the broad heading of factor analysis. The aim of the PCA is to reduce the large amount of data storage to the size of the feature space that is required to represent the data economically. The wide 1-D pixel vector made of the 2-D face picture in compact main elements of the space function is designed for facial recognition by the PCA. This is called a projection of self-space. The proper space is determined with the identification of the covariance matrix’s own vectors, which are centered on a collection of fingerprint images. I build a camera-based real-time face recognition system and set an algorithm by developing programming on OpenCV, Haar Cascade, Eigenface, Fisher Face, LBPH, and Python.

The ever-increasing volume in the collection of image data in various fields of science, medicine, security and other fields has brought the necessity to extract knowledge. Face classification/recognition is one of the challenging problems of computer vision. This paper presents details development of a real time face recognition system (FRS) aimed to operate in less constrained environment. Firstly, it is reviewed the well-known techniques used in face recognition then the details of every step in recognition process and explains the ideas, which leaded to these techniques. Being widely used in pattern recognition tasks, neural networks have also been applied in face recognition. In this study, we developed a face recognition system based on Step Error Tolerance Back-propagation Neural Network (SET-BPN). SET-BPNs supply flexibility and straightforward design by reducing error in each step of learning which make the system easily and rapidly operable along with the successful classification results. In order to analyze the system in practice we ran several tests using real data. Empirical results show that proposed approach greatly improves recognition speed in feature matching step. From the experiment it is found that the system correctly recognizes 91% of the faces, using less then one second of test samples from each face image.

In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same ( 90 % ) when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system.

Face Recognition is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. This work introduces a new real time face Recognition system. The process is broken into four major steps: (1) database collection, (2) face recognition to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92.Second step, efficient real time face recognition algorithm is then used to recognize faces with a known database. For real time face Recognition, VGG-16 with Transfer Learning and Convolutional Neural Network (CNN) are used. This proposed system is implemented using keras. Lastly the performance of these two classifiers is measured using of precision, recall, F1-score, and accuracy.

Presently, facial image recognition via a thermal camera is a critical phase in numerous fields. Systems using thermal facial images suffer from numerous problems in face identification. In this paper, a model Edge-Aided Generative Adversarial Network (EA-GAN) is introduced to overcome the difficulties of thermal face identification by synthesizing a visible faces image from the thermal version. To enhance the performance of the Conditional Generative Adversarial Network (CGAN) model for the create realistic face images, the edge information extracted from the thermal image has been used as input, thus lead to improving overall the system's achievement. Moreover, a new model is presented in the present work for face identification by integrating two Convolutional Neural Networks (CNN) to achieve high and rapid accuracy rates. Based on the experiments on the Carl dataset for faces, it is indicated that EA-GAN can synthesize visually comfortable and identity-preserving faces; thus, better performance is achieved in comparison with the state-of-the-art approaches for thermal facial identification.

The problem of facial image recognition (identification) is presented. The difference between facial image recognition and identification is shown. To solve the identification problem, a model of object reprint in the image was developed. This model solves the problem by representing the object in 3-dimensional form, which makes it possible to evaluate and form the necessary characteristics of the object in full, whereas in 2-dimensional form, it is impossible to do this. The model of an object reprint in an image can be used to create a reprint of any spatial objects. To train a reprint model of an object in an image, a multi-layer neural network is used, which is trained sequentially. A local detector for the facial image identification model has been developed to account for acceptable changes in angle, various noise, and different light levels. The binary value that is the result of model processing, represented as activation, determines the relation of a particular image to the corresponding class. The local detector is not only the main element of the model of the object reprint in the image, but it is also a separate mathematical construction. It accepts input data as two-dimensional images. The developed model of object reprint on the image completely solves the problem of identifying a person from the facial image as a whole in conditions of interference and regardless of changes in the angle.

This paper introduces an efficient method for face localization and recognition in color images. The proposed method uses the location of eyes for computation and extraction of a face's bounding ellipse. In this way, parameters of a face's ellipse (center, orientation, major and minor axis), is computed by the location of eyes in a face image. In the next step, we apply Pseudo Zernike Moments (PZM), Zernike Moments (ZM) and Principal Component Analysis (PCA) for feature extraction. For classification of these feature vectors a new structure of RBF neural networks with a novel distance function is introduced and a new method for determination of RBF unit parameters is proposed. Finally, we compare the efficiency of the proposed system for three types of feature vectors (PZM, ZM and PCA). Results emphasize the high accuracy and efficiency of the PZM features proportion to other features (ZM and PCA) for use in the proposed recognition system.

For the robust recognition of noisy face images, this paper proposed an improved fast neighborhood component analysis (FNCA) method by introducing a spatially smooth regularizer (FNCA-SSR). The SSR can penalize large differences between adjacent pixels by enforcing local spatially smoothness, and makes FNCA-SSR model robust to Gaussian and pepper-salt noises in face image. Experimental results on the ORL and FERET face data sets show that, for the recognition of noisy face images, FNCA-SSR is very robust and can achieve much higher recognition accuracy than FNCA and other subspace methods.KeywordsFace recognitionMetric learningNeighborhood components analysisRobust recognition

Hybrid meta-heuristic algorithm based deep neural network for face recognition

This paper proposes a real-time face recognition system based on the Compute Unified Device Architecture (CUDA) platform, which effectively completed the face detection and recognition tasks. In the face detection phase with Viola-Jones cascade classifier, we implemented and improved novel parallel methodologies of image integral, calculation scan window processing and the amplification and correction of classifiers. In the face recognition phase, we explored the parallelizing of the algorithm and parallelized some part of the testing phase. Through the optimization of the two important part of face recognition system, the system we proposed make a big difference. The experimental results demonstrate that, in comparison with traditional CPU program, the proposed approach running on an NVidia GTX 570 graphics card could respectively achieve 22.42 times speedup in detection phase and 1668.56 times speedup in recognition phase when only training 2000 images and testing 40 images compared with the CPU program running on an Intel core i7 processor. The recognition speed will increase until it reaches the hardware resource limit. It shows that the system we proposed achieves a good real-time performance.

Many face recognition tasks have been carried out in controlled environments, compared to controlled environments face recognition in uncontrolled environments like pose variant images is the most challenging task. The main aim of this work is to recognize the faces in pose variant images with different arbitrary rotations using two different machine learning algorithms Alexnet convolutional neural network (ACNN) and Support Vector Machine (SVM) are trained with 20 samples of pose variant images dataset each, totally 40 samples to recognize the person.: From Matlab simulation results ACNN has achieved better accuracy(96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) compared to SVM(89%). Attained Significance value (P!O.000) using SPSS analysis. From this study it shows that ACNN has higher accuracy than SVM.

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

This paper pertains to the recognition of human face within the color images. The proposed scheme used the clue of skin color to detect faces within the color images, and Mallat wavelet transform was applied to get the LL2 frequency subband image of the original faces, then, features were extracted from the raw data by using principal component analysis (PCA). These discriminative and low dimensional features achieved were fed to a support vector machines (SVMs) with polynomial kernel for the training and fuzzy theory was applied on multi-class classification to deal with the unclassifiable cases. Experiments were conducted using the indoors photographs and the results demonstrated that the proposed methodology was efficient for the frontal faces detection and recognition.

Facial recognition has been used in several areas such as security, detection of criminals or suspects, etc. The face is considered the most important part of the human body in the person identification process since it can be considered the most important perspective in human identification. This paper proposed a real-time monitoring security system for high secured areas rather than relying on human security officers. The proposed system engages with capturing and detecting the human face using the real-time image segment captured by the web camera through the Viola-Jones algorithm. After detecting the face in the captured image, face identification can be made through the LBP by finding the similarities. Once the identification is made, automatically it recognizes the face. Open Computer Vision (OpenCV) is one of the most efficient machine learning libraries used for Object Detection. OpenCV library is used for detection, and Haar - Cascades are trained to detect certain objects. LBP is used for the face recognition process.

With the rapid development of the era of artificial intelligence, our lives have become more comfortable, and "face scanning" using facial recognition technology has become a new way of life. Facial recognition is a biometric technology that uses devices such as cameras to take photos containing faces, recognize faces in photos, and obtain information about facial features to match. Facial recognition technology belongs to a broad category of biometric technologies used by government and private institutions to identify people. The system includes the collection and recognition of facial images, extraction of key points, image processing, extraction of facial features, and comparison of facial recognition results. This article describes the advanced multiband Retinex algorithm, which allows processing images with uneven lighting and is integrated into the Yolov5 object detection pipeline. To evaluate this method, a dataset was collected from photographs of 3,045 students in various lighting scenarios with controlled changes in illumination achieved using a software light source. This method preserves image details and increases contrast, resulting in better detection accuracy while maintaining computational efficiency. The experimental results showed that the proposed approach can be more effective than traditional methods of obtaining images of faces in uneven lighting conditions.