Analysis and Performance Evaluation of ICA-Based Architectures for Face Recognition

Singha, Anu; Bhowmik, Mrinal Kanti; Dhar, Prasenjit; Ghosh, Anjan

doi:10.1007/978-3-319-26145-4_24

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Finally, the number of features for these methods depends on the database. • ICA method was based on independent component analysis [51]. This method used the same number of features for different databases, but its performance was degraded.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, they provide the lowest performances and their number of features are comparatively high. Finally, the number of features for these methods depends on the database. ICA method was based on independent component analysis [51]. This method used the same number of features for different databases, but its performance was degraded. The method based on Markov random field (MRF) [52] has very high dimensionality and its recognition rate is very low; therefore, this method is not satisfactory. The projection method did not mention how many frequency components have been extracted from each octet.…”

Section: Resultsmentioning

confidence: 99%

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Genetic algorithm‐optimised structure of convolutional neural network for face recognition applications

Rikhtegar

Pooyan

Shalmani

2016

IET Computer Vision

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Proposing a proper method for face recognition is still a challenging subject in biometric and computer vision applications. Although some reliable systems were introduced under relatively controlled conditions, their recognition rate is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. To alleviate these problems, a hybrid face recognition system is proposed which benefits from the superiority of both convolutional neural network (CNN) and support vector machine (SVM). To this end, first a genetic algorithm is employed to find the optimum structure of CNN. Then, the performance of the system is improved by replacing the last layer of CNN with an ensemble of SVMs. Finally, using concepts of error correction, decision is made. The potential of CNN as a trainable feature extractor provides a flexible recognition system that can recognise faces with variations in pose and illumination. Simulation results show that the system achieves good recognition rate and is robust against variations in terms of facial expressions, occlusion, noise, and illuminations.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Genetic algorithm‐optimised structure of convolutional neural network for face recognition applications

Rikhtegar

Pooyan

Shalmani

2016

IET Computer Vision

View full text Add to dashboard Cite

show abstract

“…The artificial neural network [1], principle component analysis (PCA) [2], independent component analysis [3], linear discriminant analysis (LDA) [4], support vector machine [5] and K-nearest neighbour [6] are the most widely used methods. In addition, the hidden Markov model (HMM) [7] has been successfully used in face recognition during the last two decades.…”

mentioning

confidence: 99%

Low-Complexity Face Recognition using a Multilevel DWT and TwoStates of Continuous HMM to recognize Noisy Images

Farhan¹,

Mahmuod²,

Al-Muifraje³

et al. 2017

IJET

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Abstract-Face recognition has become an important subject in modern life, especially in security and surveillance applications. This work introduces a face recognition method, which is characterized by high-speed, low-complexity, and high-efficiency in a noisy environment. The performance of this method is greatly improved by using a median filter, such that each image is filtered to eliminate the influence of noise and light illumination. Multiple levels of discrete wavelet transform are applied to the filtered image to reduce size and eliminate further noise. Subsequently, the resultant image is scanned using a window with a predefined overlap in raster fashion to construct a sequence of observation vectors used as the basis of a model. I. INTRODUCTION Face recognition has recently received considerable attention due to its important applications in many areas and fields such as credit card verification, security, and surveillance. The advantage of face recognition over other biometric techniques is the mechanism to recognize persons, which is frequently performed without requiring that the person have physical contact with the device, e.g. use of palm or fingers. The face image can be captured by a camera without informing the target person, especially in the case of criminal surveillance and apprehension.The methods introduced to recognize faces vary relative to the procedure used for feature extraction and type of classifier used. The artificial neural network [1], principle component analysis (PCA) [2], independent component analysis [3], linear discriminant analysis (LDA) [4], support vector machine [5] and K-nearest neighbour [6] are the most widely used methods. In addition, the hidden Markov model (HMM) [7] has been successfully used in face recognition during the last two decades. The advantage of applying HMM to face recognition is the flexibility of the selection of training models, such that it is easy to add or remove individuals. There is no need for re-training the overall system; however, a model with updated images must be re-trained.For dimensionality reduction, a particular feature extraction method is applied to form a sequence of observation vectors to model faces. Thereafter, for each individual, one HMM model is constructed by training a specified set of images for that individual. The training process depends on the estimation of the HMM parameters [8], such that the process is iterated several times before it converges. In the testing process, the algorithm computes the probability of the observations of unknown face image calibrated with the parameters derived from the training. The unknown face belongs to the person whose model computes the highest probability.The disadvantages of using more states of the HMM are: high computational complexities, large memory requirements, and low processing speed. The proposed work addresses the unfavourable features by using only two states of the HMM.

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Cardiotocography Fetal Health Data Analysis Using Machine Learning

Singha

Vanitha²

2023

Lecture Notes in Networks and Systems

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Analysis and Performance Evaluation of ICA-Based Architectures for Face Recognition

Cited by 5 publications

References 12 publications

Genetic algorithm‐optimised structure of convolutional neural network for face recognition applications

Genetic algorithm‐optimised structure of convolutional neural network for face recognition applications

Low-Complexity Face Recognition using a Multilevel DWT and TwoStates of Continuous HMM to recognize Noisy Images

Cardiotocography Fetal Health Data Analysis Using Machine Learning

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