FACIAL EMOTION DETECTION USING OPEN CV

Automatic Face Detection innovations have made numerous upgrades in evolving world. Brilliant ATTENDANCE SYSTEM utilizing ongoing face acknowledgment is a genuine world arrangement which accompanies everyday exercises of taking care of understudies participation. The administration of participation framework can be an extraordinary weight on educators in case it is finished by hands.To determine this issue we utilize auto and brilliant participation framework which is by and large executed with the assistance of biometric called Face Detection. The primary execution steps utilized in this kind of framework are face location and perceiving the identified countenances. Face Detection is an interaction where the framework will actually want to recognize the human faces which will be caught by the camera. Here , we execute a computerized participation the board framework for understudies of the class by utilizing face acknowledgment method..

Hand and face somatotopy shown using MRI-safe vibrotactile stimulation with a novel Soft Pneumatic Actuator (SPA)-Skin interface

The exact somatotopy of the human facial representation in the primary somatosensory cortex (S1) remains debated. One reason that progress has been hampered is due the methodological challenge of how to apply automated vibrotactile stimuli to face areas in a manner that is: 1) reliable despite different curvature depending on the face location; and 2) MR-compatible and free of MR-interference artefacts when applied in the MR head-coil. Here we overcame this challenge by using soft pneumatic actuator (SPA) technology. SPAs are made of a soft silicon material and can be in- or deflated by means of airflow, have a small diameter, and are flexible in structure, enabling good skin contact even on curved body surfaces (as on the face). Here, we aimed to provide a methodological advance by providing automated tactile vibration stimulation inside the head-coil of the MRI. As a sanity check, we first mapped the well-characterised S1 finger layout using this novel device. We found that tactile stimulation of the fingers elicited characteristic somatotopic finger activations in S1, validating the use of our SPA-setup to map somatotopic representations. Ultimately, we used the device to automatically and systematically deliver somatosensory stimulation to different face locations. We found that the forehead representation was least distance from the representation of the hand. Within the face representation, we found that the lip representation is most distant from the forehead representation, with the chin represented in between. Together our results show that, by providing vibrotactile stimulation using the SPA-technology, we are able to reveal clear somatotopic representational patterns.

Masked Face Detection Algorithm in the Dense Crowd Based on Federated Learning

Wearing masks is an effective and simple method to prevent the spread of the COVID-19 pandemic in public places, such as train stations, classrooms, and streets. It is of positive significance to urge people to wear masks with computer vision technology. However, the existing detection methods are mainly for simple scenes, and facial missing detection is prone to occur in dense crowds with different scales and occlusions. Moreover, the data obtained by surveillance cameras in public places are difficult to be collected for centralized training, due to the privacy of individuals. In order to solve these problems, a cascaded network is proposed: the first level is the Dilation RetinaNet Face Location (DRFL) Network, which contains Enhanced Receptive Field Context (ERFC) module with the dilation convolution, aiming to reduce network parameters and locate faces of different scales. In order to adapt to embedded camera devices, the second level is the SRNet20 network, which is created by Neural Architecture Search (NAS). Due to privacy protection, it is difficult for surveillance video to share in practice, so our SRNet20 network is trained in federated learning. Meanwhile, we have made a masked face dataset containing about 20,000 images. Finally, the experiments highlight that the detection mAP of the face location is 90.6% on the Wider Face dataset, and the classification mAP of the masked face classification is 98.5% on the dataset we made, which means our cascaded network can detect masked faces in dense crowd scenes well.

Sex differences in age- and episodic memory-related effects on task-based functional connectivity

Healthy aging is associated with episodic memory decline. However, little is known about sex differences in the effect of normative aging on memory-related brain network dynamics. Here, we used a data-driven multivariate partial least squares (PLS) connectivity analysis to identify similarities and differences in the effect of biological sex on age- and memory-related differences in task-based fMRI connectivity during encoding and retrieval of face-location associations (spatial context memory). Aging was associated with episodic memory decline in both sexes, but there were no significant sex or sex-by-age interactions in memory performance. The connectivity results show that men exhibited greater between-network connectivity with advanced age, which was detrimental to memory performance. Women exhibited reduced connectivity between visual and higher order cognitive networks with advanced age, which was detrimental to memory performance. Therefore, there are sex differences in the effect of age on episodic memory-related connectivity.

Image Classification Using Deep Neural Networks

Facial expression recognition is an activity that is performed by every human in their day-to-day lives. Each one of us analyses the expressions of the individuals we interact with to understand how people interact and respond with us. The malicious intentions of a thief or a person to be interviewed can be recognized with the help of his facial features and gestures. Face recognition from picture or video is a well-known point in biometrics inquiry. Numerous open places, for the most part, have reconnaissance cameras, and these cameras have their noteworthy security incentives. It is generally recognized that face recognition has assumed a significant job in reconnaissance framework. The genuine favorable circumstances of face-based distinguishing proof over different biometrics are uniqueness. Since the human face is a unique item having a high level of inconstancy in its appearance, face location is a troublesome issue in computer vision. This chapter explores emotion detection using facial images.

A Multi-Modal Gaze Tracking Algorithm

A person’s emotional information, needs and cognitive processes can be described by eye movement states and concerns, so gaze tracking was first applied in the field of psychology. With the continuous development of information technology, the application range of gaze tracking has expanded from psychology to medical, military, commercial and many other fields. Aiming at the problem of high misjudgment rate and long time-consuming of traditional iris location methods, this paper proposes a gaze tracking method based on human eye geometric characteristics to improve the tracking accuracy in 2D environment. First, the human face is located by face location algorithm and the position of human eye is estimated roughly. Then the iris template is built by iris image, and the iris center location algorithm is used to locate the iris center position. Finally, the eyes corners and iris center points are extracted to locate the eye area accurately and obtain the binocular image. The binocular images are input into the feature extraction network as multi-modal information in parallel, and the convoluted feature channels are reconstructed using the weight redistribution module in the network. Then the reconstructed features are fused in the full connection layer. Finally, the output layer is used to classify the reconstructed features. Experiments were carried out on a self-built screen block dataset. For 12 classified data, the lowest recognition error rate is 5.34%.

Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa

The current presence system can be done with a computerized system, one of which is the face biometric system. This study focuses on the application of position estimation and tracking based on clustering on people's faces to determine the position in three dimensions. Position estimation can be obtained by making a kernel that is ready to be used to predict three-dimensional coordinates of faces based on two-dimensional coordinates of two images. Position estimation can be done by utilizing the Machine Learning algorithm family. In this study, Least Absolute Shrinkage and Selection Operators (LASSO) is used to perform the position estimation. Meanwhile, clustering in this study uses the K-Means algorithm. Based on the test results, the kernel error obtained in estimating the face location is 9.23 cm. The tracking accuracy of an object based on clustering is 100%.

Various Strategies and Procedures Utilized for Gender Acknowledgment Utilizing Voice and Its Highlights

As discourse is viewed as one of the significant pieces of the people, as it is supportive for understanding the feelings and sentiments and so forth of the individual talking. The bio measurements innovation these days are particularly mainstream like unique mark and so forth. However, the upgrade of this innovation has additionally come like face location, iris identification, and voice recognition and so on. Voice acknowledgment is utilized for the sexual orientation recognizable proof also in light of the fact that it is been considered as one the dependable method of sex distinguishing proof. Voice acknowledgment is fundamentally understanding the voice signals and convert them into little examples and the machine is been will be been prepared to recognize those examples. The paper tells about the voice acknowledgment and the different strategy utilized for it

An Engel Curve for Variety

I examine the source and welfare implications of differences in household consumption diversity. I document the existence of a positive correlation between household variety and expenditure to motivate a simple framework where households purchase more varieties to counteract diminishing returns to quantity but face location-specific costs of accessing variety. Estimating the model with Indian household data, I find that the increase in dietary diversity between 1983 and 2009 was mostly due to lower costs of accessing variety, which resulted in large welfare gains. Urban households also benefit from a lower cost of accessing varieties than rural households.

Performance Analysis of Face Detection system using HOG and QualHOG Features

Inspired by the expansion of minimal effort advanced cameras in cell phones being conveyed in computerized systems, we think about the connection between perceptual picture quality and an exemplary PC vision errand of face recognition. We measure the corruption in execution of a well known and compelling face detector when human-saw image quality is corrupted by twists usually happening in catch, stockpiling, and transmission of facial pictures, including clamor, obscure, and pressure. It is observed that, inside a certain scope of picture quality, an unobtrusive increment in picture quality can radically enhance face recognition execution. These outcomes can be utilized to guide asset or transfer speed distribution in securing or correspondence/conveyance frameworks that are connected with face location undertakings. In this work a perceptual quality QualHOG feature is used. Face locators prepared on these new components give measurably huge change in resilience to picture bends over a solid gauge. Distortion dependent which is more distorted uninformed variations of the face indicators are proposed and assessed on a huge database of face pictures speaking to an extensive variety of mutilations. A one-sided variation of the preparing calculation is additionally recommended that further improves the power of these face locators. To encourage this exploration, we have developed another dataset in our lab for further study.