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

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Gerçek Zamanlı Yüz Tespit Etme Ve Arduino Kartı İle Haberleşme

Tezsiz Yüksek Lisans Bitirme ProjesiSon yıllarda, görüntülerde insan yüzünün algılanması araştırmacılar için ilgi çekici hale gelmiştir. Artan bu ilginin arkasındaki sebeplerden biri daha hızlı, gelişmiş ve havaalanlarında, stadyumlarda, hastanelerde ve fabrikalarda güvenliğin sağlanması için daha emniyetli bir düzen ihtiyacıdır. Hızlı ve etkin bir araç olan bilgisayarlar ile yüz algılama ve yüzün yerinin belirlenmesini kullanan birçok uygulama, hayatın zaruri bir parçası olmuştur. Bu çalışmada, görüntü işleme teknikleri ve yüz bulma için Haar-Cascades Sınıflandırıcısı kullanılarak bir görüntünün yüz içerip içermediğinin tespiti, resim üzerinde yüz yerlerini saptama işlemleri gerçekleştirilmiştir. Bu teknikler uygulanırken açık kaynaklı kütüphane (open source library - OpenCv) aracından yararlanılmıştır

Encoding and recognition of faces based on the human visual model and DCT

For encoding and recognizing human faces in monochrome images, we propose a new method based on a combination of the discrete cosine transform (DCT), principal component analysis (PCA), and the characteristics of the Human Visual System. The novel aspect of the proposed non-Bayesian, approach is that, in the course of the recognition of face images, we also achieve image compression (in the form of encoding). With the help of examples, we demonstrate the superiority and advantages of the new method in comparison with the results found in the literature