Similarity of Inference Face Matching On Angle Oriented Face Recognition

Face recognition is one of the wide applications of image processing technique. In this paper complete image of face recognition algorithm is proposed. In the prepared algorithm the local information is extracted using angle oriented discrete cosine transforms and invokes certain normalization techniques. To increase the Reliability of the Face detection process, neighborhood pixel information is incorporated into the proposed method. Discrete Cosine Transform (DCT) are renowned methods are implementing in the field of access control and security are utilizing the feature extraction capabilities. But these algorithms have certain limitations like poor discriminatory power and disability to handle large computational load. The face matching classification for the proposed system is done using various distance measure methods like Euclidean Distance, Manhattan Distance and Cosine Distance methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on image database which is acquired under variable illumination and facial expressions. It is observed from the results that use of face matching like various method gives a recognition rate are high while comparing other methods. Also this study analyzes and compares the obtained results from the proposed Angle oriented face recognition with threshold based face detector to show the level of robustness using texture features in the proposed face detector. It was verified that a face recognition based on textual features can lead to an efficient and more reliable face detection method compared with KLT (Karhunen Loeve Transform), a threshold face detector. Keywords: Angle Oriented, Cosine Similarity, Discrete Cosine Transform, Euclidean Distance, Face Matching, Feature Extraction, Face Recognition, Image texture features

Video-based driver identification using local appearance face recognition

In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each person’s distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images. Index Terms — Local appearance face recognition, vehicle environment, discrete cosine transform, fusion. 1

FACE RECOGNITION USING DEEP NEURAL NETWORKS WITH THE COMBINATION OF DISCRETE WAVELET TRANSFORM, STATIONARY WAVELET TRANSFORM, AND DISCRETE COSINE TRANSFORM METHODS

Personal identification can be done by using face, fingerprint, palm prints, eye’s retina, or voice recognition which commonly called as biometric methods. Face recognition is the most popular and widely used among those biometric methods. However, there are some issues in the implementation of this method: lighting factor, facial expression, and attributes (chin, mustache, or wearing some accessories). In this study, we propose a combination method of Discrete Wavelet Transform and Stationary Wavelet Transform that able to improve the image quality, especially in the small-sized image. Moreover, we also use Histogram Equalization in order to correct noises such as over or under exposure, Discrete Cosine Transform in order to transform the image into frequency domain, and Deep Neural Networks in order to perform the feature extraction and classify the image. A 10-fold cross-validation method was used in this study. As the result, the proposed method showed the highest accuracy up to 92.73% compared to Histogram Equalization up to 80.73%, Discrete Wavelet Transform up to 85.85%, Stationary Wavelet Transform up to 64.27%, Discrete Cosine Transform up to 89.50%, the combination of Histogram Equalization, Discrete Wavelet Transform, and Stationary Wavelet Transform up to 69.77%, and the combination of Stationary Wavelet Transform, Discrete Wavelet Transform, and Histogram Equalization up to 77.39%

Face Recognition Using Discrete Cosine Transform for Global and Local Features

Face Recognition using Discrete Cosine Transform (DCT) for Local and Global Features involves recognizing the corresponding face image from the database. The face image obtained from the user is cropped such that only the frontal face image is extracted, eliminating the background. The image is restricted to a size of 128 x 128 pixels. All images in the database are gray level images. DCT is applied to the entire image. This gives DCT coefficients, which are global features. Local features such as eyes, nose and mouth are also extracted and DCT is applied to these features. Depending upon the recognition rate obtained for each feature, they are given weightage and then combined. Both local and global features are used for comparison. By comparing the ranks for global and local features, the false acceptance rate for DCT can be minimized.Comment: face recognition; biometrics; person identification; authentication; discrete cosine transform; DCT; global local features; Proceedings of the 2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering (IConRAEeCE) IEEE Xplore: CFP1153R-ART; ISBN: 978-1-4577-2149-

Image Restoration Effect on DCT High Frequency Removal and Wiener Algorithm for Detecting Facial Key Points

This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing low-frequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK

The Effect of Using Histogram Equalization and Discrete Cosine Transform on Facial Keypoint Detection

This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing low-frequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK

Similarity of Inference Face Matching On Angle Oriented Face Recognition

Abstract: Face Recognition is a well-known image processing technique that has been used in many applications like Law enforcement security, Bio-Metric Systems etc. In this paper complete image of face recognition algorithm is proposed. In the prepared algorithm the local information is extracted using angle oriented discrete cosine transforms and invokes certain normalization techniques. To increase the Reliability of the Face detection process, neighborhood pixel information is incorporated into the proposed method. Also this study analyzes and compares the obtained results from the proposed Angle oriented face recognition with threshold based face detector to show the level of robustness using texture features in the proposed face detector. It was verified that a face recognition based on textual features can lead to an efficient and more reliable face detection method compared with KLT (Karhunen Loeve Transform), a threshold face detector. Keywords: Angle Oriented, Euclidian Distance, Face Recognition, Feature Extraction, Image texture features

Face recognition/clustering - performance improvements, 2007

This thesis will introduce Face Recognition as an important and crucially needed type of biometrics. The existing and most widely used Face Recognition algorithms have been tested and the results will be presented. Additionally, the limitations of the existing FF methods will be discussed, focusing chiefly on the future of Face Recognition and the reasons such relatively poor results were achieved in comparison with results from other Biometrics. Finally, a novel system that enhances the performance of the face matching for existing FF algorithms (High-speed k-means Image Clustering using the Discrete Cosine Transform and its comparison with existing methods) will be discussed. Appendix A focuses on the results obtained from the Haar face detection algorithm, utilizing different face databases. Appendix B is dedicated to the Matlab code

Analisis dan perancangan perangkat lunak pengenalan wajah dengan menggunakan algoritma bacterial foraging optimization

ABSTRAKSI: Wajah merupakan salah satu tanda pengenal yang dimiliki masing-masing orang, setiap orang mempunyai wajah unik dan mempunyai karakter rmasing-masing. Dalam dunia teknologi masa depan, diinginkan suatu teknologi yang dapat meminimalisir gap antara teknologi dan user. Teknologi diingikan dapat mengenali dan berinteraksi dengan usernya. Pengenalan wajah diharapkan dapat menjembatani dan mengurangi gap yang telah ada.Penelitian ini menggunakan algoritma Bacterial Foraging Optimization (BFO) secara utuh dengan menerapkan segala langkah dari algoritma BFO meliputi kemotaksis, tumbling, Reproduksi, dan juga Eliminasi. Dengan menggunakan Discrete Cosine Transform (DCT) sebagai ekstraksi fitur dan K-Nearest Neighbor sebagai sistem klasifikasi dg mengkhususkan pada Euclidean distance, Cosine Distance, dan Cityblock. Penelitian ini dilakukan secara offline dengan menggunakan Matlab sebagai sarana simulasi dari kinerja algoritma yang sedang diuji. Pada penelitian ini, penggunaan BFO lebih kepada optimasi pemilihan ciri yang akan digunakan sebagai unsur pengenalan suatu citra.Dari penelitian yang telah dilakukan, didapatkan hasil akurasi pengenalan wajah dengan menggunakan BFO adalah sebesar 87,33% dengan jumlah ciri yang digunakan adalah sebesar 200 ciri. Sedangkan hasil akurasi terbaik dari DCT adalah sebesar 89% dengan menggunakan ciri sebesar 10304. Dengan menggunakan hanya 4% dari ciri keseluruhan hasil dari ekstraksi ciri dari DCT, pengenalan wajah ini berhasil dioptimasi dengan baik walaupun dengan akurasi yang masih kalau dari hasil DCT. Sedangkan penggunaan klasifikasi terbaik dalam penelitian ini adalah dengan menggunakan klasifikasi Euclidean distance pada kedua kasus dengan hasil akurasi sebesar 89% dan 87%.Kata Kunci : Pengenalan Wajah, Bacterial Foraging Optimization, Discrete Cosine Transform, Euclidean Distance, K-Nearest Neighbor, Euclidean Distance, Cityblock, Cosine DistanceABSTRACT: Face is one of the important biometric that everyone have. Every human have their own face pattern. It\u27s something that could make people unique and could be recognized by other people. In the future, technology desired to relieve the gap between user and the tools. Desirable technology that could interact with the user and knowing each other. That\u27s why face recognition is important here.This research studied about face recognition using Bacterial foraging optimization (BFO) Algorithm. in this research used all the steps in BFOA like chemotaxis, tumbling, reproduction, and elimination and dispersal. Using Discrete Cosine Transform (DCT) as the feature extraction and K-Nearest Neighbor (K-NN) as the classification that consists of Euclidean distance, Cosine Distance and cityblock. this research based on offline sistem using MATLAB as the simulation software and ORL database as the training and testing image. BFOA mostly used as feature selection in this research and only used to select the feature that extracted by DCT.The results of the research is the face recognition accuracy using BFO is 87.33% with only 636 features selected. And the best accuracy using DCT is 89% by using 10304 features. with this result, BFO function as feature selector has been met with only used less than 2% features from DCT feature extraction but with not as good as accuracy of DCT\u27s result. in this research, also state that Euclidean distance is the best classifier in both condition (BFO and DCT) with the accuracy is 89% and 87.33 percent.Keyword: Face recognition, Bacterial Foraging Optimization, Discrete Cosine Transform, Euclidean Distance, K-Nearest Neighbor, Euclidean Distance, Cityblock, Cosine Distance