Face Detection and Recognition, Face Emotion Recognition Through NVIDIA Jetson Nano

Sati, Vishwani; Sánchez, Sergio Márquez; Shoeibi, Niloufar; Arora, Ashish; Corchado, Juan M.

doi:10.1007/978-3-030-58356-9_18

Cited by 36 publications

(27 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jetson Nano has been integrated with various technologies to enhance its performance for face recognition. For example, in the study [10], the authors integrate Jetson Nano with OpenCV to improve the performance of a face recognition system. They found that the integration improved the accuracy of the system and reduced the processing time.…”

Section: Literature Reviewmentioning

confidence: 99%

Real Time Face Recognition System using Jetson Nano

2023

5th ISSE National Conference (INAC-05) on Systems Approach for Self-Reliance in Advanced Technologies

View full text Add to dashboard Cite

This work aims to deploy an accurate face recognition system on a low cost, portable and reliable hardware. Nvidia Jetson Nano is a small size, portable and powerful computer to run Neural Networks. It uses an integrated 128-core Maxwell GPU for parallelization. The total power consumed by the device is less than 10 watts. Face recognition system consists of 2 steps, Face detection and Identification. Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Model has been used for Face detection and VGG-Face has been used for Face Identification. The network is trained on VGG-Face dataset which consists of 2.6 million face images of 2,622 people. The system has achieved real time performance of upto 4 fps with an accuracy of 97.6% on Labeled Faces in the Wild (LFW) dataset.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Real Time Face Recognition System using Jetson Nano

2023

5th ISSE National Conference (INAC-05) on Systems Approach for Self-Reliance in Advanced Technologies

View full text Add to dashboard Cite

show abstract

“…Sati et al [ 18 ] in this paper implemented face detection and recognition and face emotion recognition through NVIDIA. Jetson Nano, in this paper implements both face recognition and face emotional detection, traditional to the present facial emotional identifications are one of the most challenging techniques, by adding some features in this technique helps to provide the better accuracy and the classification results.…”

Section: Review Of the Literaturementioning

confidence: 99%

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Arora

Chaubey²,

Raman

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

show abstract

“…Sati et al [19] suggested Face Detection and Recognition, Face Emotion Recognition through NVIDIA Jetson Nano. NVIDIA Jetson Nano, ideally suited as an IoT edge device because of its small size and connectivity options.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Attendance Monitoring System Using Deep Learning

Shanmuhappriya¹,

G²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

View full text Add to dashboard Cite

The manual method of attendance management is time consuming and difficult to maintain. So, the process of management of attendance in schools and universities should be automated. Many biometric systems can be used to record attendance. Face recognition is used frequently. The proposed work aims to record attendance without human interference. In this method the camera is fixed in the classroom.Faces are detected from the captured image and then recognized using trained model. The attendance is then marked and verified by class teacher. The attendanceis marked and absenteeism is sent to the parents. The proposed tooluses Deep Convolution Neural Network(DCNN) in which Max-Margin Object Detection(MMOD) and Histogram of Orientation Gradient(HOG) in Dlib used for face detection. Facenet algorithm responsible for extracting high quality features from the given faces is used for face recognition. The model is trained using K-Nearest Neighbour(KNN) and the framework is loaded in Jetson nano for testing.

show abstract

Face Detection and Recognition, Face Emotion Recognition Through NVIDIA Jetson Nano

Cited by 36 publications

References 10 publications

Real Time Face Recognition System using Jetson Nano

Real Time Face Recognition System using Jetson Nano

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Automatic Attendance Monitoring System Using Deep Learning

Contact Info

Product

Resources

About