Real-Time DNN-Based Face Identification for the Blind

The study of image processing in today’s world and the booming possibility of building a smart classroom and a smarter campus with the help of aided vigilance and surveillance is slowly moving from a thought that can be considered for the future to an actual real-world implementation. In modern day schools and university campuses there is an increasing demand for a real-time monitoring and quick responding database that tracks the student activities. This is not always required but serves as a one-click system that handles average information searches and returns the list of fast action options that are available in tracking the campus activities that come in the purview of its span. In a diverse educational campus comprising of several branches and streams that share a single campus, there is a possible chance of intrusion and unauthorized entry which may lead to undesirable and unnecessary loss of intellectual property and manipulation of identity. In a particular academic unit, there would be a surveillance system that monitors and tracks these activities and offers to its privileged users a response in real-time. It can also be used to track the attendance of students in an automated fashion which leads to a digitized and paperless approach. It can be said that this will be implemented in its entirety to a campus unit. Face identification is an essential step in face recognition, in which one of the typical of a class and authoritative application in visual sensor network. Visual perception is one of the physical measurements based on secured features. Face identification is a demanding assignment, because it has to scan and match against a library of known faces. E.g. lighting condition, different posture, various kind of body languages.

Download Full-text

Real-Time Face Identification via CNN and Boosted Hashing Forest

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ◽

10.1109/cvprw.2016.25 ◽

2016 ◽

Cited By ~ 5

Author(s):

Yury Vizilter ◽

Vladimir Gorbatsevich ◽

Andrey Vorotnikov ◽

Nikita Kostromov

Keyword(s):

Real Time ◽

Face Identification

Download Full-text

Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest

Deep Learning for Biometrics - Advances in Computer Vision and Pattern Recognition ◽

10.1007/978-3-319-61657-5_2 ◽

2017 ◽

pp. 33-55

Author(s):

Yury Vizilter ◽

Vladimir Gorbatsevich ◽

Andrey Vorotnikov ◽

Nikita Kostromov

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Face Identification

Download Full-text

AutisMitr: Emotion Recognition Assistive Tool for Autistic Children

Open Computer Science ◽

10.1515/comp-2020-0006 ◽

2020 ◽

Vol 10 (1) ◽

pp. 259-269

Author(s):

Akansha Singh ◽

Surbhi Dewan

Keyword(s):

Emotion Recognition ◽

Real Time ◽

Facial Feature ◽

Recognition System ◽

Face Identification ◽

Positive Outcomes ◽

Autistic Children ◽

Facial Feature Extraction ◽

Three Stages

AbstractAssistive technology has proven to be one of the most significant inventions to aid people with Autism to improve the quality of their lives. In this study, a real-time emotion recognition system for autistic children has been developed. Emotion recognition is implemented by executing three stages: Face identification, Facial Feature extraction, and feature classification. The objective is to frame a system that includes all three stages of emotion recognition activity that executes expeditiously in real time. Thus, Affectiva SDK is implemented in the application. The propound system detects at most 7 facial emotions: anger, disgust, fear, joy, sadness, contempt, and surprise. The purpose for performing this study is to teach emotions to individuals suffering from autism, as they lack the ability to respond appropriately to others emotions. The proposed application was tested with a group of typical children aged 6–14 years, and positive outcomes were achieved.

Download Full-text