No / In this paper we present a new method for accurate real-time facial feature detection. Our method is based on local feature detection and enhancement. Previous work in this area, such as that of Viola and Jones, require looking at the face as a whole. Consequently, such approaches have increased chances of reporting negative hits. Furthermore, such algorithms require greater processing power and hence they are especially not attractive for real-time applications. Through our recent work, we have devised a method to identify the face from real-time images and divide it into regions of interest (ROI). Firstly, based on a face detection algorithm, we identify the face and divide it into four main regions. Then, we undertake a local search within those ROI, looking for specific facial features. This enables us to locate the desired facial features more efficiently and accurately. We have tested our approach using the Cohn-Kanade’s Extended Facial Expression (CK+) database. The results show that applying the ROI has a relatively low false positive rate as well as provides a marked gain in the overall computational efficiency. In particular, we show that our method has a 4-fold increase in accuracy when compared to existing algorithms for facial feature detection.

No / The purpose of this chapter is to discuss and present a computational framework for detecting and analysing facial expressions efficiently. The approach here is to identify the face and estimate regions of facial features of interest using the optical flow algorithm. Once the regions and their dynamics are computed a rule based system can be utilised for classification. Using this framework, we show how it is possible to accurately identify and classify facial expressions to match with FACS coding and to infer the underlying basic emotions in real time.

Integration of audio and video signals for automatic speech recognition has become an important field of study. The Audio-Visual Speech Recognition (AVSR) system is known to have accuracy higher than audio-only or visual-only system. The research focused on the visual front end and has been centered around lip segmentation. Experiments performed for lip feature extraction were mainly done in constrained environment with controlled background noise. In this thesis we focus our attention to a database collected in the environment of a moving car which hampered the quality of the imagery. We first introduce the concept of illumination compensation, where we try to reduce the dependency of light from over- or under-exposed images. As a precursor to lip segmentation, we focus on a robust face detection technique which reaches an accuracy of 95%. We have detailed and compared three different face detection techniques and found a successful way of concatenating them in order to increase the overall accuracy. One of the detection techniques used was the object detection algorithm proposed by Viola-Jones. We have experimented with different color spaces using the Viola-Jones algorithm and have reached interesting conclusions. Following face detection we implement a lip localization algorithm based on the vertical gradients of hybrid equations of color. Despite the challenging background and image quality, success rate of 88% was achieved for lip segmentation.

This master’s Thesis focuses on image processing on Android platform and development of an application, that is able to do face detection and recognition in real scene. Thesis gives highlight of modern algorithms of face detection. It first examines and compares the standard features of Android platform (FaceDetector a FaceDetectionListener) and JJIL, OpenIMAJ, OpenCV libraries experiment, and presents the results. For purposes of face recognition was selected OpenCV library. Three different algorithms of identification were tested: FisherFaces, EigenFaces a Local Binary Patterns Histograms. Based on performance comparison best methods were implemented in developed application.

In the semestral thesis is given basic methods of non-contact measurement heart rate. There is explained Eulerian video magnification method deals with the visualization of the pulse in the videosequence of face. The semestral thesis describes algorithm Viola-Jones face detection in images and algorithm Kanade-Lucas-Tomasi for tracking faces in the videosequence. Part of the work includes design and realization of measurement. There is explained realization of the program and documented execution results, which are discussed. From the results it is designed to guide for optimal recording.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-10-01T15:06:04Z No. of bitstreams: 1 MATHEUS BEZERRA ESTRELA RODRIGUES - DISSERTAÇÃO PPGCC 2012..pdf: 7068761 bytes, checksum: 4b1283a1da5ca466fcf0357c33091a30 (MD5) / Made available in DSpace on 2018-10-01T15:06:04Z (GMT). No. of bitstreams: 1 MATHEUS BEZERRA ESTRELA RODRIGUES - DISSERTAÇÃO PPGCC 2012..pdf: 7068761 bytes, checksum: 4b1283a1da5ca466fcf0357c33091a30 (MD5) Previous issue date: 2012-02-29 / Impulsionado pelo crescimento no uso de vigilância eletrônica, essa pesquisa introduz o uso de uma técnica que demonstra eficiência no reconhecimento de faces em imagens, alterando o objeto de busca para pneus de veículos, visando o reconhecimento da presença do veículo na cena. A técnica aplicada para o reconhecimento é o algoritmo Viola-Jones. Essa técnica é dividida em dois momentos: o treinamento e a detecção. Na primeira etapa, vários treinamentos são executados, usando aproximadamente 7000 imagens diferentes. Para a etapa final, um detector de faces foi adaptado para reconhecer pneus, utilizando o treinamento da etapa anterior, e sua eficiência em reconhecer os pneus foi comparável à eficiência do detector de faces que usa treinamento de referência da biblioteca em software que é referência nesta área, OpenCV. O detector desenvolvido apresentou taxa de reconhecimento de 77%, quando o reconhecimento de faces obteve 80%. A taxa de falsos negativos também foi próxima, apresentando o detector de pneus 2% e o de faces 1%. / Motivated by the growing use of electronic surveillance, this research introduces the use of the Viola-Jones algorithm, which is known to be efficient in recognition of human faces in images, changing the object to be recognized to vehicle tires, aiming to detect vehicles in a scene. This approach divides the process in two steps: training and detection. Training was done using around 7000 different images of vehicles. For the detection step, work was done to adapt a face detector to detect vehicles tires. The tire detector was compared to a face detector that used a reference training for faces from OpenCV library. The tire detector showed 77% efficiency, whereas the face detector showed 80%. False negative numbers also showed similar closeness, as 2% for the tire detector and 1% for the reference face detector.