Optimizing Face Recognition Using PCA

Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially for big size database. This paper conducts a study to optimize the time complexity of PCA (eigenfaces) that does not affects the recognition performance. The authors minimize the participated eigenvectors which consequently decreases the computational time. A comparison is done to compare the differences between the recognition time in the original algorithm and in the enhanced algorithm. The performance of the original and the enhanced proposed algorithm is tested on face94 face database. Experimental results show that the recognition time is reduced by 35% by applying our proposed enhanced algorithm. DET Curves are used to illustrate the experimental results.Comment: 9 page

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa

Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN). Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components. It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features. The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization. The core part of the CNN-based approach is a revised VGG network. We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor. The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table

Hubungan gaya pembelajaran dengan pencapaian akademik pelajar aliran vokasional

Analisis keputusan Sijil Pelajaran Malaysia (SPM) 2011 menunjukkan penurunan pencapaian bagi Sekolah Menengah Vokasional. Oleh itu, kajian ini dilaksanakan bertujuan untuk mengkaji hubungan di antara gaya pembelajaran dengan pencapaian akademik pelajar. Kajian ini juga ingin mengenalpasti gaya pembelajaran paling dominan yang diamalkan oleh pelajar serta melihat perbezaan gaya pembelajaran dengan jantina pelajar. Seramai 131 orang Pelajar Tingkatan Empat Kursus Vokasional Di Sekolah Menengah Vokasional Segamat di Johor telah terlibat dalam kajian ini. Soal selidik Index of Learning Style (ILS) yang dibangunkan oleh Felder dan Silverman (1991) yang mengandungi 44 soalan telah digunakan untukh menjalankan kajian ini. Gaya pembelajaran pelajar dapat dilihat melalui empat dimensi gaya pembelajaran yang terdiri dari dua sub-skala yang bertentangan iaitu dimensi pelajar Aktif dan Reflektif, dimensi pelajar Konkrit dan Intuitif, dimensi pelajar Verbal dan Visual, serta dimensi pelajar Tersusun dan Global. Data yang diperolehi dianalisis dengan menggunakan perisian Statistical Package for Social Science for WINDOW release 20.0 (SPSS.20.0). Ujian Korelasi Pearson digunakan untuk menganalisis data dalam mengkaji hubungan gaya pembelajaran dengan pencapaian akademik pelajar. Nilai pekali p yang diperolehi di antara gaya pembelajaran dengan pencapaian pelajar adalah (p=0.1 hingga 0.4). Ini menunjukkan tidak terdapat hubungan yang signifikan di antara dua pembolehubah tersebut. Kajian ini juga mendapati bahawa gaya pembelajaran yang menjadi amalan pelajar ialah gaya pembelajaran Tersusun. Hasil kajian juga mendapati bahawa tidak terdapat perbezaan yang signifikan di antara gaya pembelajaran dengan jantina pelajar