Diabetic Retinopathy Detection Using Machine Learning
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Monocular Depth Estimation using a Multi-grid Attention-based Model
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Construction of Efficient Smart Voting Machine with Liveness Detection Module
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Real Time Sign Language Recognition and Speech Generation
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Analysis of Artificial Intelligence based Image Classification Techniques
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Design of ANN Based Machine Learning Method for Crop Prediction
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A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
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COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
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Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
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State of Art Survey on Plant Leaf Disease Detection
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Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
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OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
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VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 3 | Issue - 3 | september 2021
Published
10 September, 2021
Face recognition at a distance (FRAD) is one of the most difficult types of face recognition applications, particularly at a distance. Due to the poor resolution of facial image, it is difficult to identify faces from a distance. Recently, while recording individuals, the camera view is broad and just a small portion of a person's face is visible in the image. To ensure that the facial image has a low resolution, which deteriorates both face detection and identification engines, the facial image is constantly at low resolution. As an immediate solution, employing a high-definition camera is considered as a simple and practical approach to improve the reliability of algorithm and perform well on low-resolution facial images. While facial detection will be somewhat decreased, a picture with higher quality will result in a slower face detection rate. The proposed work aims to recognize faces with good accuracy even at a distance. The eye localization works for the face and eye location in the face of a human being with varied sizes at multiple distances. This process is used to detect the face quickly with a comparatively high accuracy. The Gaussian derivative filter is used to reduce the feature size in the storage element, which improves the speed of the recognition ratio. Besides, the proposed work includes benchmark datasets to evaluate the recognition process. As a result, the proposed system has achieved a 93.24% average accuracy of face recognition.
KeywordsFace recognition Gaussian derivative filter
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