A novel non-linear modifier for adaptive illumination normalization for robust face recognition

Vishwakarma, Virendra P.; Dalal, Sahil

doi:10.1007/s11042-019-08537-6

Cited by 36 publications

(21 citation statements)

References 37 publications

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“…As KELM is a non-iterative learning algorithm of ANN, the proposed method is faster than the other existing contemporary methods of illumination normalization in terms of computation time. M) where M is the number of pixels in the input face image [27]. The fixed number of large scale DCT coefficients is processed by modified S MF.…”

Section: Resultsmentioning

confidence: 99%

Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations

Vishwakarma

Dalal

2018

ICST Transactions on Scalable Information Systems

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The multifaceted light varying environment severely degrades the performance of person recognition using facial images. Here, the authors present a novel person identification method using hybridization of artificial neural network (ANN) and fuzzy logic concepts. An efficient illumination normalization method is presented with the help of a new modified S membership function. The proposed method of illumination normalization retains the large scale facial features as well as suppresses the variations related to change in light variations. Kernel extreme learning machine (KELM) which is a nonlinear and non-iterative learning algorithm of ANN is used for classification. Various kernel types and parameters are experimented to find the best choice for robust classification. To assess the performance of proposed hybridization, Yale and extended Yale B face databases have been used. Very promising results have been achieved which establish the worth of the proposed method.

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Section: Resultsmentioning

confidence: 99%

Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations

Vishwakarma

Dalal

2018

ICST Transactions on Scalable Information Systems

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“…From Figure 14, the new activation function still performs well. At the same time, a face attitude dataset (CMU-PIE) [48] is used to further verify the influence of the new activation function on the convolutional neural network model, which can be seen in Figure 15. CMU-PIE face dataset includes 40,000 photos from 68 people, in five poses.…”

Section: Extensibilitymentioning

confidence: 99%

The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition

et al. 2020

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The convolutional neural network (CNN) has been widely used in image recognition field due to its good performance. This paper proposes a facial expression recognition method based on the CNN model. Regarding the complexity of the hierarchic structure of the CNN model, the activation function is its core, because the nonlinear ability of the activation function really makes the deep neural network have authentic artificial intelligence. Among common activation functions, the ReLu function is one of the best of them, but it also has some shortcomings. Since the derivative of the ReLu function is always zero when the input value is negative, it is likely to appear as the phenomenon of neuronal necrosis. In order to solve the above problem, the influence of the activation function in the CNN model is studied in this paper. According to the design principle of the activation function in CNN model, a new piecewise activation function is proposed. Five common activation functions (i.e., sigmoid, tanh, ReLu, leaky ReLus and softplus–ReLu, plus the new activation function) have been analysed and compared in facial expression recognition tasks based on the Keras framework. The Experimental results on two public facial expression databases (i.e., JAFFE and FER2013) show that the convolutional neural network based on the improved activation function has a better performance than most-of-the-art activation functions.

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“…Morphological features include QRS pattern recognition while computing expansion coefficients using matching pursuits algorithm gave time frequency correlation. The heartbeats are taken from MIT-BIH arrhythmia database and four local sets of GLS and classified using k nearest neighbour classifier 22 , 23 . Very good accuracy was achieved with both the descriptors 24 .…”

Section: Introductionmentioning

confidence: 99%

Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

Dalal

Vishwakarma

2021

Sci Rep

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Every human being has a different electro-cardio-graphy (ECG) waveform that provides information about the well being of a human heart. Therefore, ECG waveform can be used as an effective identification measure in biometrics and many such applications of human identification. To achieve fast and accurate identification of human beings using ECG signals, a novel robust approach has been introduced here. The databases of ECG utilized during the experimentation are MLII, UCI repository arrhythmia and PTBDB databases. All these databases are imbalanced; hence, resampling techniques are helpful in making the databases balanced. Noise removal is performed with discrete wavelet transform (DWT) and features are obtained with multi-cumulants. This approach is mainly based on features extracted from the ECG data in terms of multi-cumulants. The multi-cumulants feature based ECG data is classified using kernel extreme learning machine (KELM). The parameters of multi-cumulants and KELM are optimized using genetic algorithm (GA). Excellent classification rate is achieved with 100% accuracy on MLII and UCI repository arrhythmia databases, and 99.57% on PTBDB database. Comparison with existing state-of-art approaches has also been performed to prove the efficacy of the proposed approach. Here, the process of classification in the proposed approach is named as evolutionary hybrid classifier.

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A novel non-linear modifier for adaptive illumination normalization for robust face recognition

Cited by 36 publications

References 37 publications

Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations

Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations

The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition

Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier

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