LDA BASED FACE RECOGNITION USING DCT AND HYBRID DWT

In this paper we present a hybrid approach for efficient human face recognition. The proposed method is based on linear discriminant analysis of image in DCT domain with a combination of details of DWT. And the similarity measure Minkowshi is used here. This approach reduces the storage requirement and computation time while preserving the data. The approach LDA -DCT-hybrid DWT is evaluated on Matlab using ORL face database. Compared to previous methods the proposed method improves feature extraction and retrieval rate

ANALISA PENGUKURAN SIMILARITAS BERDASARKAN JARAK MINIMUM PADA PENGENALAN WAJAH 2D MENGGUNAKAN DIAGONAL PRINCIPAL COMPONENT ANALYSIS

Pengenalan identitas berdasarkan citra biometri semakin ditingkatkan dalam sistem keamanan pemerintahan. Sudah banyak penelitian yang telah dilakukan untuk meningkatkan penyelesaian permasalahan tersebut. Pengenalan identitas berdasarkan biometri dapat dilakukan menggunakan citra wajah, citra sidik jari, dan citra retina. Citra biometri wajah lebih memungkinkan untuk diterapkan karena tidak menggunakan alat khusus untuk pengambilan citra biometri dan dapat diterima oleh masyarakat. Citra biometri wajah dapat diperoleh dengan menggunakan kamera digital pada umumnya. Penelitian ini melakukan analisa berbagai metode pengukuran similaritas pengenalan citra biometri wajah 2D. Metode yang digunakan antara lain jarak euclidean, manhattan, SSE-distance, MSE-distance, Canberra, dan modified-SSE. Ekstraksi fitur citra wajah menggunakan metode Diagonal Principal Component Analysis (diaPCA), merupakan pengembangan dari 2DPCA. Metode ini menggabungkan informasi piksel baris dan piksel kolom dari suatu citra. Berdasarkan uji coba yang telah dilakukan dapat diambil kesimpulan bahwa jarak-SSE memberikan hasil error rata- rata yang paling minimum dibandingkan dengan metode pengukuran jarak yang lain, yaitu 0,75% atau dengan kata lain mencapai akurasi sebesar 99,25%. Sedangkan metode pengukuran jarak yang memiliki error paling besar adalah jarak Euclidean, yaitu mencapai error rata-rata 11,83% atau akurasi 88,17%. Pembandingan dengan penelitian sebelumnya yang menggunakan PCA, menunjukkan bahwa secara umum nilai akurasi diaPCA lebih tinggi daripada PCA. Kata kunci: pengenalan wajah, diagonal PCA, pengukuran jarak minimu

Assembled matrix distance metric for 2DPCA-based face and palmprint recognition

Author name used in this publication: David ZhangBiometrics Research Centre, Department of ComputingVersion of RecordPublishe

A Computational Model of Quantitatively Measuring the Alzheimer's Disease Progression in Face Identification

There are numerous large-scale biomedical and pharmacological research projects to study Alzheimer’s Disease (AD), and potential drugs and therapeutic interventions to improve this severe disease. Of significant importance are life quality of AD patients.In particular, AD patient’s ability to recognize intimate family members and nurses’ faces largely decides their life quality. The broad objective of this research is focused on providing methods to determine the extent of disease progress from the viewpoint of recovering as much cognitive ability as possible.Specifically, this research would computerize the AD patient’s diseased brain and retrained the brain with focus on recovering the visual recognition ability of family member and medical care personnel. Likewise, potential recommendations for the patients’ family members and others who interact with the patients, in order to help improve quality of life and daily interactions

Autonomous Vision Based Facial and voice Recognition on the Unmanned Aerial Vehicle

The development of human navigation and tracking in the real time environment will lead to the implementation of more advanced tasks that can performed by the autonomous robots. That means, we proposed new intelligent algorithm for human identification using difficult of facial and speech which can substantially improve the rate of recognition as compared to the biometric identification for Robust system development. This project system that can recognize face using Eigenface recognizer with Principal component analysis (PCA) and human voice using the Hidden Markov Model(HMM) and. Also in this paper, combinations of algorithms such as modified Eigenface, Haar-Cascade classifier, PCA and HMM resulted in a more robust system for facial and speech recognition. The proposed system was implemented on AR drone 2.0 using the Microsoft Visual Studio 2015 platform together with EmguCV. The testing of the proposed system carried out in an indoor environment in order to evaluate its performance in terms of detection distance, angle of detection, and accuracy of detection. 500 images of different people were used for face recognition at detection distances. The best average result of 92.22% was obtained at a detection

Weighted Fisher Discriminant Analysis in the Input and Feature Spaces

Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the pairs of classes are treated the same way, some classes are closer than the others. Weighted FDA assigns weights to the pairs of classes to address this shortcoming of FDA. In this paper, we propose a cosine-weighted FDA as well as an automatically weighted FDA in which weights are found automatically. We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights. Our experiments on the ORL face recognition dataset show the effectiveness of the proposed weighting schemes.Comment: Accepted (to appear) in International Conference on Image Analysis and Recognition (ICIAR) 2020, Springe

A Recommender System for Online Consumer Reviews

Online consumer reviews have helped consumers to increase their knowledge about different products/services. While most previous studies try to provide general models that predict performance of online reviews, this study notes that different people look for different types of reviews. Hence, there is a need for developing a system that that is able to sort reviews differently for each user based on the ratings they previously assigned to other reviews. Using a design science approach, we address the above need by developing a recommender system that is able to predict the perceptions of each user regarding helpfulness of a specific review. In addition to addressing the sorting problem, this study also develops models that extract objective information from the text of online reviews including utilitarian cues, hedonic cues, product quality, service quality, price, and product comparison. Each of these characteristics may also be used for sorting and filtering online reviews