A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed Local Binary Pattern Methodology

Ruang server merupakan ruang yang menyimpan aset-aset dan data-data penting dari suatu perusahaan sehingga keamanan untuk akses keluar masuk ruang server perlu diperhatikan agar menghindari kejadian yang tidak diinginkan. Pada saat ini sudah banyak dikembangkan sistem keamanan hingga kunci konvensional, RFID, serta sistem keamanan menggunakan teknologi biometrik seperti sidik jari, iris, dan juga wajah yang memiliki karakteristik berbeda setiap wajahnya sehingga diharapkan bisa menjadi sistem keamanan yang handal. Seiring berkembangnya teknologi membuat seseorang semakin mudah mengakses internet untuk mendapatkan data-data biometrik seperti wajah yang dapat di gunakan untuk pemalsuan atau spoofing untuk mendapatkan akses ilegal ke suatu ruangan. Penelitian sistem keamanan ini menggunakan pegenalan wajah (face recognition) dan liveness sebagai anti- spoofing dan metode Local Binary Pattern dan Convolution Neural Network untuk meningkatkan sistem keamanan agar terhindar dari pemalsuan wajah. Hasil penelitian ini mendapatkan keakuratan pendeteksian wajah asli atau palsu sebesar 90% dan akurasi sistem dalam mengenali wajah sebesar 93.3%. Kesalahan proses pengenalan wajah terjadi 5 kali dan kesalahan saat proses pengenalan wajah dan 2 kali saat pengenalan wajah asli, dari 4 skenario dengan 40 kali uji coba. Sistem keamanan pada penelitian ini 95% bekerja dengan baik dan sesuai dengan perencanaan

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Application Research of Deep Convolutional Neural Network in Computer Vision

Journal of Networking and Telecommunications ◽

10.18282/jnt.v2i2.886 ◽

2020 ◽

Vol 2 (2) ◽

pp. 23

Author(s):

Lei Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computer Vision ◽

Face Recognition ◽

Human Brain ◽

Image Classification ◽

Convolutional Neural Networks ◽

Convolution Neural Network ◽

Data Set ◽

Deep Convolution Neural Network

<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of "abstract concepts". Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.</p>

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Unconstrained face recognition using deep convolution neural network

International Journal of Information and Computer Security ◽

10.1504/ijics.2020.105183 ◽

2020 ◽

Vol 12 (2/3) ◽

pp. 332

Author(s):

Amrit Kumar Agrawal ◽

Yogendra Narain Singh

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolution Neural Network ◽

Deep Convolution Neural Network ◽

Unconstrained Face Recognition ◽

Unconstrained Face

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Human face recognition based on adaptive deep Convolution Neural Network

2016 35th Chinese Control Conference (CCC) ◽

10.1109/chicc.2016.7554454 ◽

2016 ◽

Cited By ~ 7

Author(s):

Liang Chen ◽

Xiaojie Guo ◽

Chenlu Geng

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolution Neural Network ◽

Human Face ◽

Human Face Recognition ◽

Deep Convolution Neural Network

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Graphite Classification Based on Improved Convolution Neural Network

Processes ◽

10.3390/pr9111995 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1995

Author(s):

Guangjun Liu ◽

Xiaoping Xu ◽

Xiangjia Yu ◽

Feng Wang

Keyword(s):

Neural Network ◽

Recognition Accuracy ◽

Recognition Algorithm ◽

Convolution Neural Network ◽

High Tech ◽

Data Set ◽

Output Module ◽

Deep Convolution Neural Network ◽

Migration Network ◽

Visual Geometry

In the development of high-tech industries, graphite has become increasingly more important. The world has gradually entered the graphite era from the silicon era. In order to make good use of high-quality graphite resources, a graphite classification and recognition algorithm based on an improved convolution neural network is proposed in this paper. Based on the self-built initial data set, the offline expansion and online enhancement of the data set can effectively expand the data set and reduce the risk of deep convolution neural network overfitting. Based on the visual geometry group 16 (VGG16), residual net 34 (ResNet34), and mobile net Vision 2 (MobileNet V2), a new output module is redesigned and loaded into the full connection layer. The improved migration network enhances the generalization ability and robustness of the model; moreover, combined with the focal loss function, the superparameters of the model are modified and trained on the basis of the graphite data set. The simulation results illustrate that the recognition accuracy of the proposed method is significantly improved, the convergence speed is accelerated, and the model is more stable, which proves the feasibility and effectiveness of the proposed method.

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