Face Recognition based on Convolutional Neural Network

Borra, Surya Prasada Rao; Pradeep, N.; Raju, Nidhin; Vineel, S.; Karteek, V.

doi:10.35940/ijeat.d6658.049420

Cited by 8 publications

(4 citation statements)

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“…Residual Neural Network (Residual Neural Network) is composed of four Chinese including Kaiming He (Kaiming He) of Microsoft. They used Resnet Unit to successfully build a 152-level neural network.The structure of Resnet50 can speed up the learning of the neural network well and improve the accuracy of the model to a great extent [6] .…”

Section: Resnet50 Modelmentioning

confidence: 99%

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Research on identity authentication and labeling technology based on MR neural network

Yang

Tang²

2023

International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022)

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Aiming at the problems of poor convenience, poor scalability, and low authentication rate in traditional authentication technology using physical contact authentication methods such as magnetic cards and passwords, this paper explores the accuracy and convenience of the practical application of MR neural network in personal identity authentication. In the MR wearable device, the neural network person identity authentication method is studied flexibly and quickly to detect and identify the person. The 3D information of the face is collected and preprocessed by the depth camera, and the MR identity authentication data set is established. The neural network Resnet model is used for face detection and face feature vector extraction, and the Euclidean method is used to compare the feature vectors and label the characters. The neural network authentication algorithm is mapped to the MR wearable device, and the deep face information in the scene is identified, matched, and labeled by using the unique spatial mapping of MR technology and the camera of the MR wearable device. It solves the problems of low flexibility, poor reliability of face information, and weak recognition stability in traditional identity authentication methods, enabling MR technology to provide a more intelligent identification and labeling method for person identity authentication.

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Section: Resnet50 Modelmentioning

confidence: 99%

“…First a convolutional layer, followed by a pooling layer, followed by a series of residual residual structures, and finally through an average pooling downsampling and a fully connected layer to get the final output. The input part is mainly composed of two parts: the largest convolution kernel and the largest pooling [6] .…”

Section: Resnet50 Modelmentioning

confidence: 99%

Research on identity authentication and labeling technology based on MR neural network

Yang

Tang²

2023

International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022)

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“…The second last layer of 2D CNN produces a probability distribution for every image location used to classify pixels into one of the initial labelled where the related object classes belong to. The classification output layer uses the cross-entropy loss of reciprocally absolute classes for multi-class classification problems [30].…”

Section: D-cnn Architecturementioning

confidence: 99%

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

Noroz

Ahmed

Kumar

et al. 2022

SJCMS

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The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.

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“…For each image location used to classify pixels into one of the initial labels where the linked object classes belong, the second final layer of 2D CNN generates a probability distribution. For multi-elegance category issues, the category output layer employs the passentropy lack of reciprocally absolute instruction [30].…”

Section: Sobel Edge Detector and Poly Roimentioning

confidence: 99%

Finger-vein Image Enhancement and 2D CNN Recognition

Baloch

Saleem

Kumar

2021

IJIST

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Finger vein recognition technology is a novel biometric technology with multiple features such as live capture, stability, difficulty in stealing and imitating, and more in the field of information security that has been utilized in a wide range of applications. In this proposed method, the finger region is separated from the background using a Sobel Edge detector and a Poly ROI which helps shape the finger. The background separation enhancement of low contrast using dual contrast limited adaptive histogram equalization which works on the visual characteristics of the finger-vein image dataset. When dual CLAHE is applied, the finger-vein histogram intensity is separated all across the image. Following the implementation of DCLAHE, an enhanced 2D-CNN model is utilized to recognize objects with the updated dataset. By maximizing the values of a preprocessed dataset, the 2D CNN model learns features. This model has a 94.88% accuracy rate.

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Face Recognition based on Convolutional Neural Network

Cited by 8 publications

References 0 publications

Research on identity authentication and labeling technology based on MR neural network

Research on identity authentication and labeling technology based on MR neural network

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

Finger-vein Image Enhancement and 2D CNN Recognition

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