标题: | 基于稀疏表示之人脸辨识在行动装置之应用 Face Recognition Based on Sparse Representation Applied to Mobile Device |
作者: | 黄冠铭 Huang, Guan-Ming 陈永平 电控工程研究所 |
关键字: | 人脸辨识;人脸认证;稀疏表示;特征脸;face recognition;face verification;sparse representation;haar-like feature;adaboost;eigneface;Android |
公开日期: | 2013 |
摘要: | 本论文旨在开发Android行动装置上之使用者人脸辨识应用程式,并将其运用于Samsung Galaxy SII智慧型手机之人脸认证系统。所开发之人脸辨识应用程式,包括人脸侦测、特征撷取、人脸辨识三部分,分别使用Viola-Jones人脸侦测程式,特征脸撷取程式,以及基于二阶范数稀疏表示之人脸辨识演算法。 由实验数据可知,不同于常见的类神经网路或支援向量机器学习法则,本论文所开发之人脸辨识演算法无需大量的资料训练时间,即可获得极高的辨识率。 各人脸资料库之实验结果,其辨识率分别为Sheffield人脸资料库之97.88%, Cohn-Kanade人脸资料库之99.44% ,ORL人脸资料库之96.5%。最后,应用于Samsung Galaxy SII智慧型手机之人脸认证应用程式,皆可于1秒之内完成人脸认证,成功地达到即时认证之实用功能。 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. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT070260002 http://hdl.handle.net/11536/75399 |
显示于类别: | Thesis |