13,575 research outputs found
Efficient smile detection by Extreme Learning Machine
Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration
Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions
Appearance-based generic object recognition is a challenging problem because
all possible appearances of objects cannot be registered, especially as new
objects are produced every day. Function of objects, however, has a
comparatively small number of prototypes. Therefore, function-based
classification of new objects could be a valuable tool for generic object
recognition. Object functions are closely related to hand-object interactions
during handling of a functional object; i.e., how the hand approaches the
object, which parts of the object and contact the hand, and the shape of the
hand during interaction. Hand-object interactions are helpful for modeling
object functions. However, it is difficult to assign discrete labels to
interactions because an object shape and grasping hand-postures intrinsically
have continuous variations. To describe these interactions, we propose the
interaction descriptor space which is acquired from unlabeled appearances of
human hand-object interactions. By using interaction descriptors, we can
numerically describe the relation between an object's appearance and its
possible interaction with the hand. The model infers the quantitative state of
the interaction from the object image alone. It also identifies the parts of
objects designed for hand interactions such as grips and handles. We
demonstrate that the proposed method can unsupervisedly generate interaction
descriptors that make clusters corresponding to interaction types. And also we
demonstrate that the model can infer possible hand-object interactions
A study on different experimental configurations for age, race, and gender estimation problems
This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation
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