Facial detection using deep learning

Abstract: Various viral epidemics have been detected such as the severe acute respiratory syndrome coronavirus and the Middle East respiratory syndrome coronavirus in the last two decades. The coronavirus disease 2019 (COVID-19) is a pandemic caused by a novel betacoronavirus called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). After the rapid spread of COVID-19, many researchers have investigated diagnosis and treatment for this terrifying disease quickly. Identifying COVID-19 from the other types of coronaviruses is a difficult problem due to their genetic similarity. In this study, we propose a new efficient COVID-19 detection method based on the K-nearest neighbors (KNN) classifier using the complete genome sequences of human coronaviruses in the dataset recorded in 2019 Novel Coronavirus Resource. We also describe two features based on CpG island that efficiently detect COVID-19 cases. Thus, genome sequences including approximately 30,000 nucleotides can be represented by only two real numbers. The KNN method is a simple and effective non-parametric technique for solving classification problems. However, performance of the KNN depends on the distance measure used. We perform 19 distance metrics investigated in five categories to improve the performance of the KNN algorithm. Some efficient performance parameters are computed to evaluate the proposed method. The proposed method achieves 98.4% precision, 99.2% recall, 98.8% F-measure, and 98.4% accuracy in a few seconds when any L 1 type metric is used as a distance measure in the KNN.

Abstract: There is a continuous increase in the amount of population over the globe, and this, in turn, increases the number of complex datasets over a period. This necessitates improving artificial intelligence algorithms for better and accurate categorization of data. The most defining characteristic of the human body of the face. Every person’s face is unique, although have the same structure such as noise, eyes, lips, etc. but it can vary strikingly. It’s within this variance which lies the distinguishing characteristics that can be used to identify one person from another. Face recognition is a popular concept which is commonly used in surveillance cameras at public places for security purposes. The "Face Recognition using DNN with LivenessNet" presents a face recognition method based on deep neural networks for liveness. Any algorithm is considered to be efficient only if it is robust and accurate. It provides accurate results with face spoofing quickly and efficiently. The main advantage of using this technique is identifying the uniqueness in the datasets by capturing the real-time face data through different modes & jitter. Also providing accurate face recognition model which can be used for safety and security purpose.

Abstract: Facial expressions are an important means of communication among human beings, as they convey different meanings in a variety of contexts. All human facial expressions, whether voluntary or involuntary, are formed as a result of movement of different facial muscles. Despite their variety and complexity, certain expressions are universally recognized as representing specific emotions - for instance, raised eyebrows in combination with an open mouth are associated with surprise, whereas a smiling face is generally interpreted as happy. Deep learning-based implementations of expression synthesis have demonstrated their ability to preserve essential features of input images, which is desirable. However, one limitation of using deep learning networks is that their dependence on data distribution and the quality of images used for training purposes. The variation in performance can be studied by changing the optimizer and loss functions, and their effectiveness is analysed based on the quality of output images obtained.

Abstract: In the last several years, face detection has been listed as one of the most engaging field in research. Face detection algorithms is used for detection of frontal human faces. Face detection find use is many applications such as face tracking, faces analysis and face recognition. In this paper, we are going to discuss face detection using a haar cascade classifier and OpenCV. In this study we would be focusing on some of the face detection technology in use.

Abstract: Every person's face is unique, although have the same structure such as noise, eyes, lips, etc. but it can vary strikingly. It’s within this variance which lies in the distinguishing characteristics that can be used to identify one person from another. Face recognition is a popular concept which is commonly used in surveillance cameras at public places for security purposes. With the advancement of digital technologies, the demand for security to provide access control is increasing. It uses various methods of authentication to keep all details secure, such as a system focused on encrypted user name & password, smart card, biometrics, etc. The “Face Recognition using DNN with LivenessNet” presents a face recognition method based on deep neural networks for liveness. Any algorithm is considered to be efficient only if it is robust and accurate. It provides accurate results with face spoofing quickly and efficiently. The main advantage of using this technique is identifying the uniqueness in the datasets by capturing the real-time face data through different modes & jitter. It provides accurate face recognition model which can be used for safety and security purpose.

Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algo- rithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a "cascade" which allows back- ground regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection perfor- mance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

Abstract: We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a new “in the wild” annotated dataset, that suggests our system advances the state-of-the-art, sometimes considerably, for all three tasks. Though our model is modestly trained with hundreds of faces, it compares favorably to commercial systems trained with billions of examples (such as Google Picasa and face.com).

Abstract: There are many excellent toolkits which provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library, targeted at both engineers and research scientists, which aims to provide a similarly rich environment for developing machine learning software in the C++ language. Towards this end, dlib-ml contains an extensible linear algebra toolkit with built in BLAS support. It also houses implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy use of these tools, the entire library has been developed with contract programming, which provides complete and precise documentation as well as powerful debugging tools.

Abstract: This paper extends the face detection framework proposedby Viola and Jones 2001 to handle profile views and rotatedfaces. As in the work of Rowley et al 1998. and Schneider-man et al. 2000, we build different detectors for differentviews of the face. A decision tree is then trained to deter-mine the viewpoint class (such as right profile or rotated60 degrees) for a given window of the image being exam-ined. This is similar to the approach of Rowley et al. 1998.The appropriate detector for that viewpoint can then be runinstead of running all detectors on all windows. This tech-niqueyields goodresults and maintainsthe speed advantageof the Viola-Jones detector. 1. Introduction There are a number of techniques that can successfullydetect frontal upright faces in a wide variety of images[11, 7, 10, 12, 3, 6]. While the definition of “frontal” and“upright”mayvaryfromsystem to system, the reality is thatmany natural images contain rotated or profile faces thatare not reliably detected. There are a small number of sys-tems which explicitly address non-frontal, or non-uprightface detection [8, 10, 2]. This paper describes progress to-ward a system which can detect faces regardless of posereliably and in real-time.This paperextendsthe frameworkproposedby Viola andJones [12]. This approach is selected because of its compu-tational efficiency and simplicity.One observation which is shared among all previous re-lated work is that a multi-view detector must be carefullyconstructed by combining a collection of detectors eachtrained for a single viewpoint. It appears that a monolithicapproach, where a single classifier is trained to detect allposes of a face, is unlearnable with existing classifiers. Ourinformal experiments lend support to this conclusion, sincea classifier trained on all poses appears to be hopelessly in-accurate.This paper addresses two types of pose variation: non-frontal faces, which are rotated out of the image plane, andnon-upright faces, which are rotated in the image plane.In both cases the multi-view detector presented in this pa-per is a combination of Viola-Jones detectors, each detectortrained on face data taken from a single viewpoint.Reliable non-upright face detection was first presentedin a paper by Rowley, Baluja and Kanade [8]. They traintwo neural network classifiers. The first estimates the poseof a face in the detection window. The second is a conven-tional face detector. Faces are detected in three steps: foreach image window the pose of “face” is first estimated; thepose estimate is then used to de-rotate the image window;the window is then classified by the second detector. Fornon-face windows, the poses estimate must be consideredrandom. Nevertheless, a rotated non-faceshouldbe rejectedby the conventional detector. One potential flaw of such asystem is that the final detection rate is roughly the productof the correct classification rates of the two classifiers (sincethe errors of the two classifiers are somewhat independent).One could adopt the Rowley et al. three step approachwhile replacingthe classifiers with those of Viola andJones.The final system would be more efficient, but not signifi-cantly. Classification by the Viola-Jones system is so effi-cient, that derotation would dominate the computational ex-pense. In principle derotation is not strictly necessary sinceit should be possible to construct a detector for rotated facesdirectly. Detection becomes a two stage process. First thepose of the window is estimated and then one ofrotationspecific detectors is called upon to classify the window.In this paper detection of non-upright faces is handledusing the two stage approach. In the first stage the pose ofeach window is estimated using a decision tree constructedusing features like those described by Viola and Jones. Inthe second stage one ofpose specific Viola-Jones dete-tectors are used to classify the window.Oncepose specific detectors are trained and available,an alternative detection process can be tested as well. In thiscase alldetectors are evaluated and the union of their de-