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This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have(More)
We introduce a novel shape-based feature set, termed the Histograms of Categorized Shapes (HCS), for robust Three-Dimensional (3D) object recognition. By adopting the sliding window approach and a linear Support Vector Machine (SVM) classifier, the efficacy of the HCS feature is assessed on a 3D ear detection task. Experimental results demonstrate that the(More)
We describe a novel approach for 3-D ear biometrics using video. A series of frames is extracted from a video clip and the region of interest in each frame is independently reconstructed in 3-D using shape from shading. The resulting 3-D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series(More)
This paper presents a framework to automatically estimate the gaze direction of an infant in an infant-parent face-to-face interaction. Commercial devices are sometimes used to produce automated measurement of the subjects' gaze direction. This approach is intrusive, requiring cooperation from the participants, and cannot be employed in interactive(More)
We present a complete, Three-Dimensional (3D) object recognition system combining local and holistic features in a computationally efficient manner. An evaluation of the proposed system is conducted on a 3D ear recognition task. The ear provides a challenging case study because of its high degree of inter-subject similarity. In this work, we focus primarily(More)
We present a complete three-dimensional (3-D) ear recognition system combining local and holistic features in a computationally efficient manner. The system is comprised of four primary components, namely: 1) ear image segmentation; 2) local feature extraction and matching; 3) holistic feature extraction and matching; and 4) a fusion framework combining(More)
Steady increases in healthcare costs and obesity have inspired recent studies into cost-effective, assistive systems capable of monitoring dietary habits. Few researchers, though, have investigated the use of video as a means of monitoring dietary activities. Video possesses several inherent qualities, such as passive acquisition, that merits its analysis(More)
In this paper, we present a fully automated approach for ear recognition based upon sparse representation. In sparse representation, features extracted from the training data of each subject are used to develop a dictionary. In this work, Gabor filters are used for feature extraction. Classification is performed by extracting features from the test data and(More)
In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using(More)
This study assessed whether specific types of object motion, which predominate in maternal naming to preverbal infants, facilitate word mapping by infants. A total of 60 full-term 8-month-old infants were habituated to two spoken words, /bæf/ and /wem/, synchronous with the handheld motions of a toy dragonfly and a fish or a lamb chop and a squiggly. They(More)