Face Recognition Process A Survey

2019 ◽  
Vol 7 (6) ◽  
pp. 999-1005
Author(s):  
Kavita Lodhi ◽  
Vandan Tewari ◽  
Priyanka Bamne
Keyword(s):  
Face Recognition ◽  
Recognition Process

scholarly journals Performance Enhancement of Face Recognition under High-Density Noise Using PCA and De-Noising Technique

2020 ◽  
Vol 33 (4) ◽  
pp. 148
Author(s):  
Nada Jasim Habeeb
Keyword(s):  
Face Recognition ◽  
Median Filter ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
High Density ◽  
Weighted Mean ◽  
Recognition Process ◽  
Adaptive Median Filter ◽  
Mean Filter

       There are many techniques for face recognition which compare the desired face image with a set of faces images stored in a database. Most of these techniques fail if faces images are exposed to high-density noise. Therefore, it is necessary to find a robust method to recognize the corrupted face image with a high density noise. In this work, face recognition algorithm was suggested by using the combination of de-noising filter and PCA. Many studies have shown that PCA has ability to solve the problem of noisy images and dimensionality reduction. However, in cases where faces images are exposed to high noise, the work of PCA in removing noise is useless, therefore adding a strong filter will help to improve the performance of recognizing faces in the case of existing high-density noise in faces images. In this paper, Median filter, Hybrid Median Filter, Adaptive Median filter, and Adaptive Weighted Mean Filter were used to remove the noise from the faces images, and they were compared in order to use the best of these filters as a pre-processing step before the face recognition process. Experimental results showed that the Adaptive Weighted Mean Filter gave better results compared with the other filters. Thus, the performance of face recognition process was improved under high-density noise using the Adaptive Weighted Mean Filter and Principal Component Analysis. For the corrupted images by 90 % noise density, Recognition rate by using Median Filter reached 0% and 33% by using Hybrid Median Filter. While Recognition rate by using the Adaptive Median Filter and Adaptive Weighted Mean Filter reached 100%.

Show Me the Features! Understanding Recognition From the Use of Visual Information

2002 ◽  
Vol 13 (5) ◽  
pp. 402-409 ◽  
Author(s):  
Philippe G. Schyns ◽  
Lizann Bonnar ◽  
Frédéric Gosselin
Keyword(s):  
Face Recognition ◽  
Visual Information ◽  
Recognition Performance ◽  
New Method ◽  
Visual Categorization ◽  
Recognition Process ◽  
The Face ◽  
Memory Representations

We propose an approach that allows a rigorous understanding of the visual categorization and recognition process without asking direct questions about unobservable memory representations. Our approach builds on the selective use of visual information in recognition and a new method (Bubbles) to depict and measure what this information is. We examine three face-recognition tasks (identity, gender, expressive or not) and establish the componential and holistic information responsible for recognition performance. On the basis of this information, we derive task-specific gradients of probability for the allocation of attention to the different regions of the face.

A Novel Approach to Improve Face Recognition Process Using Automatic Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 42-66
Author(s):  
Yacine Gafour ◽  
Djamel Berrabah ◽  
Abdelkader Gafour
Keyword(s):  
Face Recognition ◽  
Facial Recognition ◽  
Real Life ◽  
Automatic Learning ◽  
New Approach ◽  
Recognition Process ◽  
Novel Approach ◽  
Lbp Descriptor

In real-life applications, the appearance of a face changes significantly due to variations in expression, lighting, aging, exposure, and occlusion, which makes face recognition difficult. We present in this article a new approach for facial recognition. This approach is based on a set of variants of the Ho-LBP descriptor that we have proposed. In fact, the presentation of the images using a set of variants of the Ho-LBP descriptor helps the classifier to learn better. In addition, these variants are combined to improve the performance of facial recognition. We evaluated the effectiveness of our approach on ORL, Extended Yale B, and Feret databases. The obtained results are very promising, especially when compared with those of existing approaches. They show that our approach improves the accuracy of facial recognition in a very efficient way and in particular to the variations of the poses and the changes of the luminance.

scholarly journals Surveillance Camera using Face Recognition for Automatic Attendance Feeder and Energy Conservation in Classroom

2019 ◽  
Vol 8 (4) ◽  
pp. 3222-3225
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Energy Conservation ◽  
Electricity Supply ◽  
Recognition Process ◽  
Surveillance Camera ◽  
Surveillance Cameras ◽  
The Face

This works gives solution to two most important problems in the universities by equipping a surveillance camera with Artificial Intelligence (AI) technology. The first problem solved is unnecessary time wastage in manual and bio-metric (fingerprint based) attendance marking for students. The second problem solved is the unnecessary electricity wastage in classrooms without occupants. Using the videos getting recorded in surveillance cameras, the number of heads detection and face recognition is done. When there is no occupants in the class, the number of heads detected will be zero. So we can cut-off the electricity supply for that classroom. With the face recognition process the attendance for the students will be get automatically marked. The Intel movidius stick does the work of face recognition and finding the head counts.

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