Nose tip region detection in 3D facial model across large pose variation and facial expression

Detecting nose tip location has become an important task in face analysis. However, for a 3D face model with presence of large rotation variation, detecting nose tip location is certainly a challenging task. In this paper, we propose a method to detect nose tip region in large rotation variation based on the geometrical shape of a nose. Nose region has always been considered as the most protuberant part of a face. Based on convex points of face surface, we use morphological approach to obtain nose tip region candidates consist of highest point density. For each point of each region candidate, a signature is generated and evaluated with trained nose tip tolerance band for matching purpose. The region that contains the point which scores the most is chosen as the final nose tip region. This method can handle large rotation variation, facial expression, combination of all rotations (yaw, pitch and roll) and large non-facial outliers. Combination of two databases has been used; UPMFace and GavabDB as training data set and test data set. The experimental results show that 95.19% nose tip region over 1300 3D face models were correctly detected

3D face registration across pose variation and facial expression using cross profile alignment

In a 3D face recognition system, face registration is usually employed to compensate the pose variation in a 3D face model. Most previous methods in 3D face registration are based on the well known global-based approach, Iterative Closest Point (ICP). The experiments are usually conducted using cleaned and frontalviewed face models, neglecting the facial variation that often occur in real-time scenarios, such as pose variation, facial expression, facial outliers and occlusion. The proposed thesis uses a local-based approach known as Cross Profile Alignment (CPA) as an alternative to the global-based approach, utilizing the facial feature of a face surface as an attempt to cater all the above problems. Among all features on a face surface, nose tip is the most commonly used feature for facial feature landmarking. It is crucial to accurately detect the nose tip as it will affect the overall performance of the registration process. Most of the presented nose tip detection algorithms were developed merely based on the assumption that the nose tip is the highest point on a face, which is not robust enough for face model under large rotation variation and having large facial outliers. Thus, as the first step prior face registration, the thesis proposed a novel nose tip region detection algorithm using localized point signature, developed specially to locate the nose tip region across various facial variation. The experiment conducted on challenging 3D face databases yields good results with 94.77% detection rate for the nose tip region detection algorithm. Based on the nose tip region location, a cross-profile is extracted and face model is compensated for rotation variation and translation displacement. The registration framework with CPA which gained accuracy rate of 93.9% when tested within 10 degrees error margin, outperforms the registration framework with ICP using Average Face Model (AFM) with accuracy rate of 87.7%, with lower processing time. The findings during this work indicate the accuracy and the reliability of the proposed registration framework towards 3D face model with challenging facial variation

Illumination compensation in pig skin texture using local-global block analysis

Variable illumination in a texture is a common problem occurs to a real-time image modalities. The imbalance illumination in a texture creates virtual regions within one image, hence it affects the performance of the classification methods because it introduced an artifact patterns or virtual regions to an image. This paper presents a method to overcome the variable illumination problem in a pig skin texture using the information in the local and global blocks. The focus of this paper is to provide a fast, reliable and safe method to stabilize the lighting in an image. Pig skin texture is selected because it has a special pattern characteristic that needs to be preserved. The results show that in terms of the fluctuations contrast amplitudes in an image, the local-global method give better results than the standard homomorphic filtering technique