كليدواژه :
ابرطيفي , استخراج ويژگي , شناسايي چهره , طبقه بندي
چكيده فارسي :
استخراج ويژگي يك گام مهم براي پردازش و تحليل داده‌هاي ابربٌعدي در مسائل شناسايي الگو است. تصاوير ابرطيفي اخذ‌شده از سنجنده‌هاي را ه دور و تصاوير چهره انسان از جمله داده‌هاي ابربعدي محسوب مي‌شوند كه با وجود تعداد نمونه آموزشي محدود، كاهش ويژگي يك گام پيش‌پردازش اساسي براي طبقه‌بندي اين گونه داده‌ها محسوب مي‌شود. در اين مقاله، به بررسي و ارزيابي روش‌هاي نوين استخراج ويژگي‌اي مي‌پردازيم كه تا كنون براي داده چهره استفاده نشده و درهمين‌اواخر براي كاهش ابعاد تصاوير ابرطيفي سنجش از دور پيشنهاد شده‌اند. در اين پژوهش، كارايي هفت روش نوين معرفي‌شده را براي داده ابرطيفي با چهار روش پركاربرد استخراج ويژگي مورد ارزيابي و مقايسه قرار خواهيم داد. نتايج آزمايش‌ها بر روي دو داده بانك Yale و ORL، برتري تعدادي از اين روش‌هاي نوين را نسبت به روش‌هاي استخراج ويژگي LDA، NWFE، MMLDA و LPP نظارت‌شده، از نظر دقت شناسايي، نشان مي‌دهند.
چكيده لاتين :
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images are one of the high dimensional data types. Because of limitation in the number of training samples, feature reduction is the important preprocessing step for classification of these types of data. Face recognition is one of the main interesting studies in human computer interaction applications. Face is among the most significant biometric characteristics which are used for identification of individuals. Before face recognition, feature reduction is an important processing step. In this paper, we apply the new feature extraction methods, which have been firstly proposed for feature reduction of hyperspectral imagery remote sensing, on the face databases for the first time. In this research, we compare the performance of seven new feature extraction methods with four state-of-the-art feature extraction methods. The proposed methods are Nonparametric Supervised Feature Extraction (NSFE), Clustering Based Feature Extraction (CBFE), Feature Extraction Using Attraction Points (FEUAP), Cluster Space Linear Discriminant Analysis (CSLDA), Feature Space Discriminant Analysis (FSDA), Feature Extraction using Weighted Training samples (FEWT), and Discriminant Analysis- Principal Component 1 (DA-PC1). The experimental results on two face databases, Yale and ORL, show the better performance of some new feature extraction methods, from the recognition accuracy point of view compared to methods such as linear discriminant analysis (LDA), non-parametric weighted feature extraction (NWFE), median-mean line discriminant analysis (MMLDA), and supervised locality preserving projection (LPP).
