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This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based...
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using Convolutional Neural networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a...
In this paper, we tackle the continuous gesture recognition problem with a two streams Recurrent Neural Networks (2S-RNN) for the RGB-D data input. In our framework, the spotting-recognition strategy is used, that means the continuous gestures are first segmented into separated gestures, and then each isolated gesture is recognized by using the 2S-RNN. Concretely, the gesture segmentation is based...
Affective computing, particularly emotion and personality trait recognition, is of increasing interest in many research disciplines. The interplay of emotion and personality shows itself in the first impression left on other people. Moreover, the ambient information, e.g. the environment and objects surrounding the subject, also affect these impressions. In this work, we employ pre-trained Deep Convolutional...
The task of the ChaLearn Apparent Personality Analysis: First Impressions Challenge is to rate/quantify personality traits of users in short video sequences. Although the validity of personality judgments from short interactions is questionable, studies show the possibility of predicting attributed traits (First Impressions) using facial [15] and acoustic [13] features. The challenge introduces a...
As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification...
Human Epithelial type-2 (HEp-2) cells are used as substrates for the detection of Anti Nuclear Antibodies (ANA) in the Indirect Immunofluorescence (IIF) test to diagnose autoimmune diseases. Pathologists in the laboratory examine the IIF slides to detect and recognize theHEp-2 cell patterns to generate the report. So, the IIF test is subjective and requires objective analysis. This paper introduces...
Reliable automatic system for Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of systemic autoimmune diseases. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to address the HEp-2 specimen classification problem. The FCN in the proposed framework was adapted from VGG-16, which was trained with ICPR 2016 dataset...
The intrinsic interactions among a video's emotion tag, its content, and a user's spontaneous response while consuming the video can be leveraged to improve video emotion tagging, but this capability has not been thoroughly exploited yet. In this paper, we propose an implicit hybrid video emotion tagging approach by integrating video content and users' multiple physiological responses, which are only...
Current research of emotion recognition from electroencephalogram (EEG) signals rarely considers common patterns embodied in multiple subjects and individual patterns for each subject simultaneously. Therefore, in this paper, we propose a novel emotion recognition approach using subjects or subject groups as privileged information, which is only available during training. First, five frequency features...
Although emotional state recognition from voice has been extensively studied, there is not much effort focusing on the online emotion recognition. Since duration and intensity of emotional experiences change over time it is hard to employ existing static transition models while monitoring emotional states especially in an online setting. To overcome this difficulty we introduce a method which incorporates...
Face alignment is an important issue in many computer vision problems. The key problem is to find the nonlinear mapping from face image or feature to landmark locations. In this paper, we propose a novel cascaded approach with bidirectional Long Short Term Memory (LSTM) neural networks to approximate this nonlinear mapping. The cascaded structure is used to reduce the complexity of this problem and...
Human gait is an important biometric feature for person identification in surveillance videos because it can be collected at a distance without subject cooperation. Most existing gait recognition methods are based on Gait Energy Image (GEI). Although the spatial information in one gait sequence can be well represented by GEI, the temporal information is lost. To solve this problem, we propose a new...
In this paper, we discuss a novel approach to incrementally construct a rule ensemble. The approach constructs an ensemble from a dynamically generated set of rule classifiers. Each classifier in this set is trained by using a different class ordering. We investigate criteria including accuracy, ensemble size, and the role of starting point in the search. Fusion is done by averaging. Using 22 data...
Facial landmark detection is a challenging task with broad applications. Many approaches have been proposed with varying degrees of success. Regression based methods update the facial point positions iteratively. The mean shape or shapes sampled from training set is often used as the initialization, which sometimes may lead to a local minimum in update due to the offset of initial positions and target...
Pedestrian detection from in-vehicle camera images for the purpose of advanced driver assistance systems is of particular importance in cases of low-resolution pedestrians, because it is desirable to detect the pedestrian as far from the vehicle as possible to effectively provide safe driving support for the driver. Most previous studies on pedestrian detection, however, have focused on pedestrians...
We propose a machine learning based approach to real-time detection and classification assistance for images from unknown environments. While systems for detecting and classifying regular structures like faces in still images are well established, the task of e. g. detecting new morphotypes/objects in an environment is much more complex. The morphotypes/objects are not guaranteed to have apriori known...
This paper addresses the problem of efficient pedestrian detection using features that are extracted by convolving feature channels with a very small number of filters. The method uses as feature channels low level features such as LUV colour and HOG, and trains a boosted decision forest on top of the learned features. The feature selection is guided by a greedy search or by an exhaustive search on...
In this paper we propose a multi-modal object recognition system that uses a two-step hypothesis verification approach to improve runtime efficiency. The system uses local and global appearance and shape features, generating many possibly competing hypotheses, which are then verified such that the scene can be optimally explained in terms of recognized object models. The introduced modification in...
Gait recognition has been proved useful in human identification at a distance. But view variance of gait feature is always a great challenge because of the difference in appearance. If the view of the probe is different from that of the gallery, one view transformation model can be employed to convert the gait feature from one view to another. But most existing models need to estimate the view angle...
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