scholarly journals Penerapan Liveness Sebagai Anti-Spoofing Citra Digital Pada Sistem Keamanan Akses Kontrol Ruang Server Berbasis Raspberry Pi

2021 ◽  
Vol 16 (2) ◽  
pp. 31
Author(s):  
Galih Rizkya Safri ◽  
Denny Irawan ◽  
Rini Puji Astutik
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Convolution Neural Network ◽  
Raspberry Pi

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

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

scholarly journals Application Research of Deep Convolutional Neural Network in Computer Vision

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>

Sistem Pengenalan Benih Padi menggunakan Metode Light Convolutional Neural Network pada Raspberry PI 4 B

2021 ◽  
Vol 7 (2) ◽  
pp. 120-126
Author(s):  
Indra Hermawan ◽  
Defiana Arnaldy ◽  
Maria Agustin ◽  
M. Farishanif Widyono ◽  
David Nathanael ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Personal Computer ◽  
Convolution Neural Network ◽  
Raspberry Pi

Baru-baru ini, metode pembelajaran mendalam dengan Convolution Neural Network (CNN) telah banyak digunakan untuk tugas klasifikasi gambar. CNN memiliki keunggulan yang tak tertandingi dalam mengekstraksi fitur gambar diskriminatif. Namun, banyak metode berbasis CNN yang ada dirancang untuk lebih dalam dan lebih besar dengan lapisan yang lebih kompleks. Sehingga membuatnya sulit untuk diterapkan pada perangkat seluler atau pada perangkat waktu nyata yang menggunakan mikrokontroler seperti raspberry pi, Arduino, dan lain sebagainya. Hal tersebut diatasi dengan menggunakan Light Convolution Neural Network (LCNN), maka perlu dilakukan percobaan untuk menguji seberapa besar perbedaan kinerja LCNN pada Personal Computer (PC) dan pada mikrokontroler raspberry pi 4 dengan sistem operasi Raspbian. Eksperimen dilakukan dengan menggunakan beberapa parameter kinerja yaitu accuracy, F-1 Score, recall, precision, dan waktu dari pengujian klasifikasi untuk mendapatkan hasil performa dari pembelajaran mendalam. Oleh karena itu, hasil dan arsitektur model akan mengkonfirmasi perbedaan kinerja di masing-masing perangkat dan menunjukkan bagaimana performa model pada perangkat yang dibatasi sumber daya atau berjalan secara waktu nyata. Pengujian menunjukkan bahwa kinerja pada raspberry pi yang merupakan alat dengan sumber daya terbatas tidak mempengaruhi kualitas pengenalan gambar, tetapi mempengaruhi waktu pemrosesan pengenalan, dikarenakan raspberry pi membutuhkan waktu proses yang lebih lama untuk melakukan satu proses pengenalan data atau foto. Hal tersebut akan mengakumulasi waktu yang dibutuhkan untuk pemrosesan data yang banyak, sehingga dapat disimpulkan bahwa raspberry pi dan alat dengan sumber daya terbatas sangat tidak efektif untuk melakukan pelatihan pengenalan dan melakukan proses pengenalan yang berisi banyak data atau foto dalam sekali prosesnya.

Neural Network-Based Approach for Face Recognition Across Varying Pose

2015 ◽  
Vol 29 (08) ◽  
pp. 1556015 ◽  
Author(s):  
Seyed Omid Shahdi ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Information ◽  
Discrete Wavelet ◽  
Face Images ◽  
The Face ◽  
The Relationship

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

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