基於局部二值化模式延伸法之低成本人臉辨識系統

Abstract

近年來，以生物識別技術為基礎的身分認證系統和物聯網應用蓬勃發展，本論文旨在開發一基於局部二值化模式延伸法的人臉辨識系統，並將其應用為門禁系統實行於小尺寸、低成本和低功耗的物聯網裝置上。所開發之系統包含人臉偵測、特徵擷取、人臉辨識三個部份，首先利用Viola-Jones人臉偵測法找出人臉的資訊，以局部二值化模式延伸法擷取人臉特徵，接著利用主程分分析將提取後的特徵資料投影至低維度空間，最後通過二階範數稀疏表示之分類器作人臉辨識和認證。本論文所提出之人臉辨識方法，於各人臉資料庫實驗結果良好，其辨識率皆可達到95%以上，甚至可於Cohn-Kanade人臉資料庫達到99%之辨識率。本論文所提之系統作為門禁系統應用於Raspberry PI 3上，可於1秒內完成人臉認證，為一即時人臉辨識系統。

In recent years, the IoT application and the biometric-based authorization become popular. This thesis proposes a face recognition system with high accuracy rate based on extended Local Binary Pattern, and applies it as an access control system on an IoT device which is always low-cost, low-power and small-footprint. The proposed face recognition system includes three parts, face detection, feature extraction and face recognition. For the face detection, the Viola-Jones face detector is adopted to find out the face information. The extended Local Binary Pattern then extracts the local features of the face. Further transform these features to a low-dimension subspace by Principle Component Analysis method. Finally, use the classification based on the sparse representation of L2 norm minimization to identify and verify the face. From the experimental results, the proposed method can achieve a high recognition rate better than 95% in several face databases, even reach 99% for the Cohn-Kanade face database. The access control system implemented on Raspberry Pi 3 is able to complete the whole face recognition in a second, which makes it indeed a real-time system.