Neuro-fuzzy fusion in a multimodal face recognition using PCA, ICA and SIFT

Bhat, Vandana S.; Pujari, Jagadeesh

doi:10.1504/ijcvr.2016.10000132

Cited by 1 publication

(1 citation statement)

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“…It can be used for security systems, for authentication in comparison to secured ID cards or passwords that are suitable to its reliability [9]. In image classification and face recognition systems, supervised learning is used by providing the sample set of data to build and store a dataset to be used in comparison [10]. Also, they keep each pre-processed human face in a  ISSN: 2252-8938 Int J Artif Intell, Vol.…”

Section: Introductionmentioning

confidence: 99%

Face detection and recognition with 180 degree rotation based on principal component analysis algorithm

Al-Ghrairi

Mohammed²,

Sameen

2022

IJ-AI

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<p>This paper presents a simple and fast recognition system with various facial expressions, poses, and rotation. The proposed system performed in two phases. Face detection is the first phase. The front and profile face detected cropped face area from the image by Viola-Jones algorithm and the right side face is detected from the image by taking the flip of the profile image. Principal component analysis (eigenfaces) algorithm is used in the recognition phase and depends on created database models used to be compared with test face image input to the recognition procedure. For training and testing the system, two sets of the image of the file exchange interface (FEI) database have been used to identify the person. The experimental result shows the effectiveness and robustness of the method used for the detection of the face and achieves high accuracy of 96%, which improves the recognition performance with low execution time. Furthermore, the accuracy of 35 trained images for recognition is 97.143% with average time execution which is (0.323657s). Also, the accuracy of 15 tested images for recognition is 93.315% with average time execution which is (0.3348s) which indicates a good and strong success and accuracy method for facial recognition.</p>

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Section: Introductionmentioning

confidence: 99%