Search

Automatic face retrieval or verification is a matter to identify whether the target person is the same person, which has been received considerable attention by researchers in computer vision. This paper proposes a method to localize a face from video sequences by considering only one shot. First, Cascade AdaBoost is applied to identify region of a face from the video sequence. The image enhancement step is followed in order to reduce the illumination variation. Image enhancement considers facial region's local mean and standard deviation. Later Singular Value Decomposition (SVD) is used to generate any number of imitated face images for each face by perturbing its n-ordered images. Consequently, the problem of face retrieval with one sample image becomes a common face retrieval problem. In addition, we use the extended t-SNE (t-Distributed Stochastic Neighbour Embedding) to extract condensed facial features. Correspondingly the computation cost is reduced largely. On the basis of the original one facial sample and extended training samples, our proposed method shows a better performance with comparison to other methods. Therefore, the proposed method is effective and competitive. The proposed method can be easily generalized to other face related tasks, such as attribute recognition, general object detection, and face validation.

Accurate vessel segmentation is a tough task for various medical images applications especially the segmentation of retinal images vessels. A computerised algorithm is required for analysing the progress of eye diseases. A variety of computerised retinal segmentation methods have been proposed but almost all methods to date show low sensitivity for narrowly low contrast vessels. We propose a new retinal vessel segmentation algorithm to address the issue of low sensitivity. The proposed method introduces a deep learning model along with pre-processing and post-processing. The pre-processing is used to handle the issue of uneven illuminations. We design a fully Convolutional Neural Network (CNN) and train it to get fine vessels observation.The post-processing step is used to remove the background noise pixels to achieve well-segmented vessels. The proposed segmentation method gives good segmented images especially for detecting tiny vessels. We evaluate our method on the commonly used publicly available databases: DRIVE and STARE databases. The higher sensitivity of 75% leads to proper detection of tiny vessels with an accuracy of 94.7%.

To assist the social interaction of deaf and hearing impaired people, efficient interactive communication tools is expected. With the growing research interest in action and gesture recognition in the last years, many successful applications for sign language recognition comprise new types of sensors including low-cost depth camera and advanced machine learning technologies. In this paper, we present a complete overview of deep learning based methodologies for sign language recognition. We discuss various types of such approaches designed for the recognition from viewpoints of available modalities provided by depth sensors, feature extraction and classification. In addition, we summarise the currently available datasets of sign language, including gestures of finger spelling and vocabulary words, which can be used as an assessing tool for those people who are learning sign languages. We then discuss the main current research works with particular interest on how they treat the different types of data, discussing their main features and identify opportunities and challenges for future research.