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In this paper, we focus on discrete expression classification using dynamic 3D sequences (4D data) recording the facial movements. A robust approach for registering 4D data is proposed and a variant of local binary patterns on three orthogonal planes is used for feature extraction. We present a fully automatic facial expression recognition pipeline. The(More)
3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for pure 2D texture images were shown sensitive to lighting condition changes. In this paper, we present a statistical model-based technique for(More)
p53 is a frequent target for mutation in human tumors, and mutant p53 proteins can actively contribute to tumorigenesis. We employed a three-dimensional culture model in which nonmalignant breast epithelial cells form spheroids reminiscent of acinar structures found in vivo, whereas breast cancer cells display highly disorganized morphology. We found that(More)
Expression profiles of primary breast tumors were investigated in relation to disseminated tumor cells (DTCs) in bone marrow (BM) in order to increase our understanding of the dissemination process. Tumors were classified into five pre-defined molecular subtypes, and presence of DTC identified (at median 85 months follow-up) a subgroup of luminal A patients(More)
— This survey focuses on discrete expression classification and facial action unit recognition performed using 3D face data, possibly including a corresponding 2D texture image. Research trends to date are summarized and the limitations of current methods are discussed. The challenges towards the development of more accurate and automated 3D facial(More)
Behavioral biometric on mobile devices has begun to gain attention in recent years and the feasibility of touch gestures as a novel biometric modality has been investigated lately. In this paper, we propose a novel Graphic Touch Gesture Feature (GTGF) to extract the identity traits from the touch traces. The traces' movement and pressure dynamics are(More)
a r t i c l e i n f o Facial expression analysis has interested many researchers in the past decade due to its potential applications in various fields such as human–computer interaction, psychological studies, and facial animation. Three-dimensional facial data has been proven to be insensitive to illumination condition and head pose, and has hence(More)
Textured 3D face models capture precise facial surfaces along with the associated textures, making it possible for an accurate description of facial activities. In this paper, we present a unified probabilistic framework based on a novel Bayesian Belief Network (BBN) for 3D facial expression and Action Unit (AU) recognition. The proposed BBN performs(More)