基於稀疏表示之人臉辨識在行動裝置之應用

Abstract

本論文旨在開發Android行動裝置上之使用者人臉辨識應用程式，並將其運用於Samsung Galaxy SII智慧型手機之人臉認證系統。所開發之人臉辨識應用程式，包括人臉偵測、特徵擷取、人臉辨識三部分，分別使用Viola-Jones人臉偵測程式，特徵臉擷取程式，以及基於二階範數稀疏表示之人臉辨識演算法。 由實驗數據可知，不同於常見的類神經網路或支援向量機器學習法則，本論文所開發之人臉辨識演算法無需大量的資料訓練時間，即可獲得極高的辨識率。 各人臉資料庫之實驗結果，其辨識率分別為Sheffield人臉資料庫之97.88%， Cohn-Kanade人臉資料庫之99.44% ，ORL人臉資料庫之96.5%。最後，應用於Samsung Galaxy SII智慧型手機之人臉認證應用程式，皆可於1秒之內完成人臉認證，成功地達到即時認證之實用功能。

This thesis develops an Android face recognition application for users on mobile device, and applies it in the face verification system of Samsung Galaxy SII smart phone. The developed face recognition application includes three parts, the face detection using the Viola-Jones face detection program, the feature extraction implemented by the eigenface features, and the face recognition based on the sparse representation of L2 norm minimization. Different to general learning methods such as Artificial Neural Network or Support Vector Machine, the developed application does not require a tremendous amount of time in data training and can achieve a high recognition rate even higher than 99%, for examples 99.2% for the Sheffield face database, 99.44% for the Cohn-Kanade face database and 96.5% for ORL face database. Finally, the face verification application proposed for the Samsung Galaxy SII smart phone indeed successfully verifies a face just in one second which makes it a real-time application.