3D-DWT and CNN Based Face Recognition with Feature Extraction Using Depth Information and Contour Map

Patil, Chanchal Mahadev; Ruikar, Sachin D.

doi:10.1007/978-3-030-16848-3_2

Cited by 2 publications

(1 citation statement)

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“…Currently, several techniques are present for face detection in image and video sequences. Most of the existing methods are based on the concept of feature extraction and dictionary learning such as small-scale illumination invariant features [9], Gabor-features [27], 3D-DWT [28] and many more [29]. On the other hand, the dictionary based learning schemes are widely adopted for face recognition systems due to their significant performance for pose variation, illumination change and varied expressions.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Various Face Detection and Tracking and Recognition Mechanisms using Machine and Deep Learning Methods

Et. al.

2021

TURCOMAT

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Recently, the security of individuals has become the prime concern for the human community. Various real-time security management systems are developed widely. Visual surveillance is considered as one of the promising technique to improve the security which helps to detect and recognize the objects. Numerous techniques have been developed for real-time video surveillance. Face detection, tracking and recognition is one of the important part of visual surveillance systems. The existing face detection schemes suffer from various challenging issues such as pose variations, illumination conditions and occlusion and many more. To overcome these issues, we have developed new schemes which includes Bayesian learning, Region Based Convolutional Neural Networks(RCNN) and GoogleNet based CNN model for face detection, tracking and recognition. In this work, we compare the performance of proposed schemes with existing schemes for different datasets to conclude the robustness of proposed approach.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Various Face Detection and Tracking and Recognition Mechanisms using Machine and Deep Learning Methods

Et. al.

2021

TURCOMAT

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Smart healthcare system-a brain-like computing approach for analyzing the performance of detectron2 and PoseNet models for anomalous action detection in aged people with movement impairments

Divya

Peter

2021

Complex Intell. Syst.

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In this era of artificial intelligence, a wide variety of techniques are available in healthcare industry especially to study about various changes happening in the human body. Intelligent assistance using brain-like framework helps to understand and analyze various types of complex data by utilizing most recent innovations such as deep learning and computer vision. Activities are complex practices, including continuous actions as well as interleaved actions that could be processed with fully interconnected neuron-like processing machine in a way the human brain works. Human postures have the ability to express different body movements in different environments. An optimal method is required to identify and analyze different kinds of postures so that the recognition rate has to be increased. The system should handle ambiguous circumstances that include diverse body movements, multiple views and changes in the environments. The objective of this research is to apply real-time pose estimation models for object detection and abnormal activity recognition with vision-based complex key point analysis. Object detection based on bounding box with a mask is successfully implemented with detectron2 deep learning model. Using PoseNet model, normal and abnormal activities are successfully distinguished, and the performance is evaluated. The proposed system implemented a state of the art computing model for the development of public healthcare industry. The experimental results show that the models have high levels of accuracy for detecting sudden changes in movements under varying environments.

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3D-DWT and CNN Based Face Recognition with Feature Extraction Using Depth Information and Contour Map

Cited by 2 publications

References 16 publications

Comparative Analysis of Various Face Detection and Tracking and Recognition Mechanisms using Machine and Deep Learning Methods

Comparative Analysis of Various Face Detection and Tracking and Recognition Mechanisms using Machine and Deep Learning Methods

Smart healthcare system-a brain-like computing approach for analyzing the performance of detectron2 and PoseNet models for anomalous action detection in aged people with movement impairments

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