Similarity Functions for Face Recognition

2015 ◽  
pp. 73-79
Keyword(s):  
Fuzzy Logic ◽  
Face Recognition ◽  
Similarity Functions ◽  
Cosine Distance

Similarity functions are not distances, but functions aimed to evaluate the similarity between two objects. Some of them relate to some other previously explained measures, such as cosine distance. Others are statistical or probabilistic, or rely on fuzzy logic. It has not been possible to provide a comprehensive table with recognition rates, as the data were to different to be compared.

scholarly journals HUMAN IDENTIFICATION BASED ON FACE RECOGNITION SYSTEM

2021 ◽  
Vol 25 (01) ◽  
pp. 80-91
Author(s):  
Saba K. Naji ◽  
◽  
Muthana H. Hamd ◽  
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Recognition System ◽  
Manhattan Distance ◽  
Distance Measurements ◽  
Local Ternary Pattern ◽  
Face Recognition System ◽  
Cosine Distance

Due to, the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance.

scholarly journals Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

2018 ◽  
Vol 7 (4) ◽  
pp. 9 ◽  
Author(s):  
Shakir F. Kak ◽  
Firas M. Mustafa ◽  
Pedro R. Valente
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Training Image ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Face Identification ◽  
Discrete Wavelet ◽  
Training Images ◽  
Matching Process ◽  
Cosine Distance

In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

FACE DETECTION AND RECOGNITION USING MAXIMUM LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS

2009 ◽  
Vol 23 (03) ◽  
pp. 433-461 ◽  
Author(s):  
MANUEL GÜNTHER ◽  
ROLF P. WÜRTZ
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Face Detection ◽  
Recognition System ◽  
Similarity Functions ◽  
Expression Variation ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Ml Estimator ◽  
Detection And Recognition

We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions and achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8% on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1 of FRGC ver2.0, the method achieved a verification rate of 72%.

scholarly journals A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

2021 ◽  
Vol 12 (6) ◽  
pp. 1037-1046
Author(s):  
T. Arul Raj, Et. al.
Keyword(s):  
Fuzzy Logic ◽  
Face Recognition ◽  
Recognition System ◽  
Biometric Recognition ◽  
Face Recognition System ◽  
Neuro Fuzzy ◽  
The Face ◽  
Face Datasets ◽  
Fuzzy Techniques ◽  
Fully Connected

Advances in technology have made life simpler in today's society by supplying us with a variety of emerging demands lacking By assessing the progressive stability of biometric recognition accuracy for newborns, biometric recognition can be used to recognize missing newborns and prevent them from being switched in higher-level hospitals.. Recognizing and authenticating newborns is a major problem in many hospitals. The face recognition system does an outstanding job of identifying and authenticating the newborn. To answer these concerns, create a face recognition device for newborns. The proposed approach improves picture consistency on a newborn's face. Our objectives are to propose a genetic, convolutional neural network, and fuzzy logic-based automated framework for newborn face recognition. As a paradigm GCNMF is suggested for real-world newborn face recognition. Convolutional, pooling, and fully-connected layers, as well as a Neuro Fuzzy layer, form the Inherited Convolutional Neuro Multi-Fuzzy. The model employs hereditary, convolutional neural networks, and fuzzy logic to deal with ambiguity and imprecision in the input configuration representation. The efficacy and outcomes of the recommended method are then analyzed using newborn face datasets and the Genetic Convolutional Neuro Multi-Fuzzy (GCNMF) Approach.

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