Face Recognition Using Golden Ratio for Door Access Control System

Gaikwad, Prajakta S.; Kulkarni, Vinayak B.

doi:10.1007/978-981-15-8391-9_16

Cited by 5 publications

(1 citation statement)

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“…Yet developing a hardware-based CV device with maximum accuracy to detect facial emotions are challenging due to deficiency of adequate data and complexity in the computation unit. A conceptual framework for the recognition of facial expressions of suspicious activity with a Raspberry-Pi (Farnell, Leeds, UK) is implemented as a part of security enhancement in a smart city [9][10][11]. However, it is examined that there are major issues in facial recognition where the videos/images don't provide accurate and reliable support for identifying the head pose, illumination variation, and subject dependence for FER [7].…”

Section: Introductionmentioning

confidence: 99%

IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

et al. 2021

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Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral).

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Section: Introductionmentioning

confidence: 99%