Binary face image recognition using logistic regression and neural network

Vanlalhruaia,; Singh, Yumnam Kirani; Singh, Navjot

doi:10.1109/icecds.2017.8390191

Cited by 14 publications

(2 citation statements)

References 11 publications

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“…In 2017, Vanlalhruaia et al, [10] have proposed a Logistic regression algorithm for face recognition system. They converted color images to gray scale, and removed noises using Local Window Standard Deviation (LWSD) and then Adaptive Thresholding to get good quality binary image.…”

Section: Biometric System Testingmentioning

confidence: 99%

Comparative study of machine learning algorithms for face recognition

Alagah Komlavi,

Chaibou,

Naroua

2024

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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Background: The fundamental need for authentication and identification of humans using their physiological, behavioral or biological characteristics, continues to be applied extensively to secure localities, property, financial transactions, etc. Biometric systems based on face characteristics, continue to attract the attention of researchers, major public and private services. In the literature, many methods have been deployed by different authors. The best performance must be found in order to be able to recommend the most effective method. So, the main objective of thisarticle is to make a comparative study of different existing techniques.Methods: A biometric system is generally composed of four stages: acquisition of facial images, preprocessing, extraction of characteristics and finally classification. In this work, the focus is on machine learning algorithms for classification. These algorithms are: Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), Logistic Regression (LR), Naive Bayesian Classification (NB: Naive Bayes’ Classifiers) and deep learning techniques such as Convolutional Neural Networks (CNN). The comparison criterion is the average performance, calculated using three performance measures: recognition rate, confusion matrix, and the Area Under Receiver Operating Characteristic (ROC) curve.Results: Based on this criterion, the performance comparison of selected machine learning algorithms, shows that CNN is the best, with an average performance of 100.00% On ORL face database. However, on the YALE database, classical algorithms such as artificial neural networks have obtained the best performances, the highest being a rate of 100%.Discussion: Deep learning techniques are very efficient in image classification as proven by the results on the ORL database. However, their inefficiency on YALE face database is due to the small size of this database which is inappropriate for some deep learning algorithms. But this weakness can be corrected by image augmentation techniques. The comparison of these results with existing state-of-the-art methods is nearly the same. Authors achieved performances of 94.82%, 95.79%, 96.15%, 96.44%, 97.27%, 98.52% and 98.95% for NB, KNN, RF, LR, ANN, SVM and CNN classifiers, respectively. Finally, in depth discussion, it is concluded that between all these approaches which are useful in face recognition, the CNN is the best classification algorithm.

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Section: Biometric System Testingmentioning

confidence: 99%

Comparative study of machine learning algorithms for face recognition

Alagah Komlavi,

Chaibou,

Naroua

2024

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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“…In [15], Where N is the figure of predictor or variables, and learning involves the value of βi. Now in case of classification the equation will provide the real worth and it is not suitable for classification technique.…”

Section: Logistic Regression Classifiermentioning

confidence: 99%

Towards detection of abnormal event and reporting for pedestrian video surveillance

Ramalingam

Patni

Mohan³

2022

ijhs

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An Anomaly can be defined as an observation that does follow the normal activities with respect to others. Anomaly is polysemy that varies in a different context. For video sequences, an anomaly can be defined in terms of motion or state of motion that obtrude concerning to state and time. This work is based on Pedestrian Video Surveillance, having a normal event as a pedestrian and abnormal event as cars, skaters, bikers, wheelchairs, etc. i.e., non-pedestrian. A comprehensive study has been carried out of many existing systems for the Anomaly detection system. Based on many existing techniques we select a method called optical flow for the feature presentation of the detection of motion and Histogram of optical flow (HOOF) for the action contour of optical flow for every time instant irrespective of scale and direction of motion. As HOOF represents the motion of an object in a region, so there may be cases that the information related to the motion is not well represented. We used these two techniques along with the labelling of the frame inorderto overcome the problem of HOOF for the classifier. And gives the improved results.

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Performance Evaluation of Different Halftone Kernels for Binary Face Recognition

Singh

Vanlalhruaia²

2020

Learning and Analytics in Intelligent Systems

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Binary face image recognition using logistic regression and neural network

Cited by 14 publications

References 11 publications

Comparative study of machine learning algorithms for face recognition

Comparative study of machine learning algorithms for face recognition

Towards detection of abnormal event and reporting for pedestrian video surveillance

Performance Evaluation of Different Halftone Kernels for Binary Face Recognition

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