A simple and efficient eye detection method in color images

International audienceIn this paper we propose a simple and efficient eye detection method for face detection tasks in color images. The algorithm first detects face regions in the image using a skin color model in the normalized RGB color space. Then, eye candidates are extracted within these regions. Finally, using the anthrophological characteristics of human eyes, the pairs of eye regions are selected. The proposed method is simple and fast, since it needs no template matching step for face verification. It is robust because it can deals with face rotation. Experimental results show the validity of our approach, a correct eye detection rate of 98.4% is achieved using a subset of the AR face database

Precise eye localization using HOG descriptors

In this paper, we present a novel algorithm for precise eye detection. First, a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations. Then, a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of preselected eyes. Finally, we compare the eye detection results with three state-of-the-art works and a commercial software. The results show that our algorithm achieves the highest accuracy on the FERET and FRGCv1 databases, which is the most complete comparative presented so far. © Springer-Verlag 2010.This work has been partially supported by the grant TEC2009-09146 of the Spanish Government.Monzó Ferrer, D.; Albiol Colomer, A.; Sastre, J.; Albiol Colomer, AJ. (2011). Precise eye localization using HOG descriptors. 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Eye-blink detection

Podstatou této bakalářské práce je popis teoretických základů metod pro detekci mrkání. Práce popisuje metody pro nalezení lidského obličeje v obraze s komplexním pozadím. Dále různé způsoby nalezení očí v obraze a jejich následné sledování. Posledním úkolem je analýza očí a vyhodnocení jestli došlo k mrknutí či ne. Jsou zde také popsány různé pomocné prostředky pro zpracování číslicového obrazu. V závěru práce je popsána praktická realizace některých zmínění metod, tedy realizace algoritmu detekující mrkající oční pár.The merits of my Bachelor's Thesis is description of the theoretical principles of methods which are used for eye-blink detection. This work discribes methods for location of human face in a frame with the comlex background. The next principes of the work are different manners how we can find eyes in the frame and its sequential tracking. The last part is the eye analysis and the evaluation whether blinking went ahead or not.There are describes different intermedia which is used for processing of the numerical frame. At the close of the work is described the practical realization of some mentioned methods, thus the realization of algorithm which detects blinking eyes pair.

Face recognition

Tato práce se zabývá detekci tváře ve statickém obrazu. Teoretická část práce je zaměřena na barevné modely využívané pro detekci kůže v obraze (RGB, HSI, YCbCr), metodami využívající barevnou složku obrázků k detekci kůže (explicitní, parametrické či neparametrické metody), metrikou obrazu, detekci hran, matematickou morfologií, metodami pro klasifikaci tváře (příznakové metody, invariantní metody, znalostní metody, metody založené na porovnávání šablon). Praktická část obsahuje konkrétní návrh a praktickou realizaci dvou algoritmů detekující barvu kůže v obraze (jednoduchá metoda založená na Cr chrominační složce a statistická metoda). Praktická část také obsahuje návrh a praktickou realizaci dvou klasifikátorů tváře (příznaková metoda a metoda porovnávání šablon).This thesis is focused on face detection in static picture. Theoretical part contains color spaces (RGB, HSI, YCbCr), methods for skin detection (explicit, parametric or non-parametric methods), image metric, edge detection, mathematical morphology, methods for classification faces (appearance-based methods, feature invariant approaches, knowledge-based methods, template matching methods). Practical part of this thesis contains concept and practical realization two algorithms for segmentation skin in static image (simple method based on Cr chroma components and statistical method). Practical part contains concept and practical realization two algorithms for classification face (appearance-based method and template matching method) too.

Face detection in image

V této práci je prezentován přehled metod detekce obličeje v obraze a jsou vysvětleny základní principy klasifikace obrazu a jeho částí. Klíčovou částí práce je představení detektoru Viola-Jones a popis jeho implementace v jazyce Matlab. Detektor Viola-Jones je v praxi nejpoužívanější metoda pro detekci obličeje v obraze, což bylo důvodem pro detailní rozbor metody a následnou realizaci. Detektor je popsán teoreticky, rozebrány jsou základní kroky algoritmu a je zdokumentován trénovací algoritmus. Na základě teoretického rozboru byl detektor implementován v jazyce Matlab. Vlastnosti detektoru byly objektivně vyhodnoceny a porovnány s dalšími dvěma implementacemi detektoru Viola-Jones.This paper presents an overview of face detection methods. Keywords and basic principles of classification of images and it’s parts are explained. Significant part of this paper is occupied with presentation of Viola-Jones detector and it’s implementation in Matlab. Detector Viola-Jones ranks among the most used methods for face detection in practice, which was the reason for detailed analysis and subsequent implementation. Detector is theoretically described, basic steps of algorithm and training algorithm are discussed. Based on theoretical analysis, detector is implemented in Matlab. Properties of implemented detector are objectively evaluated and compared with of two different implementations.

Face Processing & Frontal Face Verification

In this report we first review important publications in the field of face recognition; geometric features, templates, Principal Component Analysis (PCA), pseudo-2D Hidden Markov Models, Elastic Graph Matching, as well as other points are covered; important issues, such as the effects of an illumination direction change and the use of different face areas, are also covered. A new feature set (termed DCT-mod2) is then proposed; the feature set utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients obtained from horizontally & vertically neighbouring blocks. Face authentication results on the VidTIMIT database suggest that the proposed feature set is superior (in terms of robustness to illumination changes and discrimination ability) to features extracted using four popular methods: PCA, PCA with histogram equalization pre-processing, 2D DCT and 2D Gabor wavelets; the results also suggest that histogram equalization pre-processing increases the error rate and offers no help against illumination changes. Moreover, the proposed feature set is over 80 times faster to compute than features based on 2D Gabor wavelets. Further experiments on the Weizmann Database also show that the proposed approach is more robust than 2D Gabor wavelets and 2D DCT coefficients

Enhanced face detection framework based on skin color and false alarm rejection

Fast and precise face detection is a challenging task in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as recognition tracking, and image database management. In the applications, face objects often come from an inconsequential part of images that contain variations namely different illumination, pose, and occlusion. These variations can decrease face detection rate noticeably. Besides that, detection time is an important factor, especially in real time systems. Most existing face detection approaches are not accurate as they have not been able to resolve unstructured images due to large appearance variations and can only detect human face under one particular variation. Existing frameworks of face detection need enhancement to detect human face under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework was proposed to improve detection rate based on skin color and provide a validity process. A preliminary segmentation of input images based on skin color can significantly reduce search space and accelerate the procedure of human face detection. The main detection process is based on Haar-like features and Adaboost algorithm. A validity process is introduced to reject non-face objects, which may be selected during a face detection process. The validity process is based on a two-stage Extended Local Binary Patterns. Experimental results on CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. As a conclusion, the proposed enhanced face detection framework in color images with the presence of varying lighting conditions and under different poses has resulted in high detection rate and reducing overall detection time

Automatic face and facial feature detection

Diplomová práce se zabývá problémem detekce obličejů v barevných statických obrazech. V práci jsou nastíněny základní pojmy, se kterými se lze při detekci obličeje setkat, a jejich vzájemné souvislosti. Jednotlivé přístupy k řešení problému detekce obličeje jsou rozděleny do skupin a blíže popsány. Z těchto přístupů se práce detailně zabývá algoritmem AdaBoost, jenž byl vybrán pro jeho pozitivní vlastnosti, kterými jsou zejména rychlost a dobré dosažené výsledky. V rámci práce byl implementován Viola-Jones detektor. Tento detektor byl natrénován na veřejně přístupné databázi obličejových obrazů a byla zkoumána možnost jeho kombinace s jednoduchým detektorem barvy kůže. Další oblastí, kterou se práce zabývá, je experimentální detekce určitých rysů obličeje.The master thesis presents an overview of face detection task in color, static images. Face detection term is posed in the context of various branches. Main concepts of face detection and also their relationships are described. Individual approaches are divided into groups and then define in turn. In the thesis is in detail described algorithm AdaBoost, which is selected on the basis of its properties. Especially speed of computation and good detection results are key features. In the scope of this work Viola-Jones detector was implemented. This detector was trained with face pictures from public accessible database. Combination of Viola-Jones detector with simple color detector is described. In the thesis is also presented experiment approach to facial features detection.

Edge-Based Automated Facial Blemish Removal

This thesis presents an end-to-end approach for taking a an image of a face and seamlessly isolating and filling in any blemishes contained therein. This consists of detecting the face within a larger image, building an accurate mask of the facial features so as not to mistake them as blemishes, detecting the blemishes themselves and painting over them with accurate skin tones. We devote the first part of the thesis to detailing our algorithm for extracting facial features. This is done by first improving the image through histogram equal- ization and illumination compensation followed by finding the features themselves from a computed edge map. Geometric knowledge of general feature positioning and blemish shapes is used to determine which edge clusters belong to correspond- ing facial features. Color and reflectance thresholding is then used to build a skin map. In the second part of the thesis we identify the blemishes themselves. A Lapla- cian of Gaussian blob detector is used to identify potential candidates. Thresholding and dilating operations are then performed to trim this candidate list down followed by the use of various morphological properties to reject regions likely to not be blem- ishes. Finally, in the third part, we examine four possible techniques for inpainting blemish regions once found. We settle on using a technique that fills in pixels based on finding a patch in the nearby image region with the most similar surrounding texture to the target pixel. Priority in the pixel fill-order is given to strong edges and contours