Offline Face Recognition System Based on GaborFisher Descriptors and Hidden Markov Models

This paper presents a new offline face recognition system. The proposed system is built on one dimensional left-to- right Hidden Markov Models (1D-HMMs). Facial image features are extracted using Gabor wavelets. The dimensionality of these features is reduced using the Fisher’s Discriminant Analysis method to keep only the most relevant information. Unlike existing techniques using 1D-HMMs, in classification step, the proposed system employs 1D-HMMs to find the relationship between reduced features components directly without any additional segmentation step of interest regions in the face image. The performance evaluation of the proposed method was performed with AR database and the proposed method showed a high recognition rate for this database

A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy

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

Continuous Wavelet Transform and Hidden Markov Model Based Target Detection

Standard tracking filters perform target detection process by comparing the sensor output signal with a predefined threshold. However, selecting the detection threshold is of great importance and a wrongly selected threshold causes two major problems. The first problem occurs when the selected threshold is too low which results in increased false alarm rate. The second problem arises when the selected threshold is too high resulting in missed detection. Track-before-detect (TBD) techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. Although TBD techniques eliminate the need for detection threshold at sensor’s signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Continuous Wavelet Transform (CWT) and Hidden Markov Model (HMM) based target detection method for employing with TBD techniques which does not employ any thresholding

DWT/PCA face recognition using automatic coefficient selection

In PCA-based face recognition, there is often a trade-off between selecting the most relevant parts of a face image for recognition and not discarding information which may be useful. The work presented in this paper proposes a method to automatically determine the most discriminative coefficients in a DWT/PCA-based face recognition system, based on their inter-class and intra-class standard deviations. In addition, the eigenfaces used for recognition are generally chosen based on the value of their associated eigenvalues. However, the variance indicated by the eigenvalues may be due to factors such as variation in illumination levels between training set faces, rather than differences that are useful for identification. The work presented proposes a method to automatically determine the most discriminative eigenfaces, based on the inter-class and intra-class standard deviations of the training set eigenface weight vectors. The results obtained using the AT&T database show an improvement over existing DWT/PCA coefficient selection techniques