An improved approach towards biometric face recognition using artificial neural network

Srivastava, Shubhi; Kumar, Ankit; Singh, Anupam; Prakash, Shiv; Kumar, Arun

doi:10.1007/s11042-021-11721-2

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…In the output probability calculation expression (7),  represents the bias term; y represents the output category, T represents transposition. On the whole, the Softmax layer is to map the multiple outputs after the original convolution to www.ijacsa.thesai.org the values 0 to 1 with the Sofmax function, and the accumulation of these values is 1, which satisfies the characteristics of probability.…”

Section:    mentioning

confidence: 99%

“…Khan AA et al built a face recognition image model on the basis of neural network and integrated genetic algorithm and principal component analysis, which provided help for face matching in forensic investigation [6]. Srivastava S et al proposed a new method of biometric authentication face recognition based on artificial neural network, which effectively reduces the error rate of face recognition [7]. Karanwal overcomes the problem of low image recognition rate in local binary patterns by proposing descriptors [8].…”

Section: Introductionmentioning

confidence: 99%

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Research on Image Algorithm for Face Recognition Based on Deep Learning

2023

IJACSA

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As people's requirements for applications are getting higher and higher, the recognition of facial features has been paid more and more attention. The current facial feature recognition algorithm not only takes a long time, but also has problems such as large system resource consumption and long running time in practical applications. Based on this, the research proposes a multi-task face recognition algorithm by combining multi-task deep learning on the basis of convolutional neural network, and analyzes its performance in four dimensions of face identity, age, gender, and fatigue state. The experimental results show that the multi-task face recognition algorithm model obtained through layer-by-layer progression takes less time than other models and can complete more tasks in the same training time. At the same time, comparing the best model M44 with other algorithms in four dimensions, it is found that the Mean Absolute Error lowest is 3.53, and the highest Accuracy value is 98.3%. On the whole, the multi-task face recognition algorithm proposed in the study can recognize facial features efficiently and quickly. At the same time, its training time is short, the calculation speed is fast, and the recognition accuracy is much higher than other algorithms. It is applied to intelligent driving behavior. Analysis, intelligent clothing navigation and other aspects have strong practical significance.

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Section:    mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Image Algorithm for Face Recognition Based on Deep Learning

2023

IJACSA

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“…The literature contains various face classification approaches that enable one to choose the group or class to which the objects belong from a small number of instances. Some of the approaches are “based on a probabilistic model, such as the Bayesian classifier and correlation [ 24 ], and others based on the regions that generate the different classes in the decision space, such as k -means [ 48 ], “Convolution Neural Network (CNN) [ 40 ], Artificial Neural Networks (ANNs) [ 1 , 39 ], support vector machines (SVMs) [ 19 , 33 ], k -Nearest Neighbours ( k -NNs), Decision Trees (DTs), and so on.…”

Section: Introductionmentioning

confidence: 99%

Designing face resemblance technique using near set theory under varying facial features

Khedgaonkar¹,

Singh²

2023

Multimed Tools Appl

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Near sets (also called Descriptively Near Sets) classify nonempty sets of objects based on object feature values. The Near Set Theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way humans perceive the similarity of objects. This paper presents a novel approach for face recognition using Near Set Theory that takes into account variations in facial features due to varying facial expressions, and facial plastic surgery. In the proposed work, we demonstrate two-fold usage of Near set theory; firstly, Near Set Theory as a feature selector to select the plastic surgery facial features with the help of tolerance classes, and secondly, Near Set Theory as a recognizer that uses selected prominent intrinsic facial features which are automatically extracted through the deep learning model. Extensive experimentation was performed on various facial datasets such as YALE, PSD, and ASPS. Experimentation demonstrates 93% of accuracy on the YALE face dataset, 98% of accuracy on the PSD dataset, and 98% of accuracy on the ASPS dataset. A detailed comparative analysis of the proposed work of facial resemblance with other state-of-the-art algorithms is presented in this paper. The experimentation results effectively classify face resemblance using Near Set Theory, which has outperformed several state-of-the-art classification approaches.

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