While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may be a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography and inertial measurement unit sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten healthy subjects performed an initial gesture recognition experiment, followed by a second experiment 1 h later and a third experiment 1 day later. Classification accuracies for the initial experiment were 92.6% and 88.8% for air and surface gestures, respectively, and there were no changes in accuracy results during testing 1 h. and 1 day later (p > 0.05). These results demonstrate the feasibility of wrist-based gesture recognition paving the way for potential future integration in to a smart watch or other wrist-worn wearable for intuitive human computer interaction.

A new face detector with very high accuracy and real-time speed for mobile phone is introduced. The method achieves the fastest speed and the highest accuracy compared with other similar methods. A series of ideas are proposed in order to accelerate detection speed of the traditional Adaboost detector. First of all, the threshold for weak classifier is learned based on a new multiple instance pruning method regarding not only positive samples but also negative samples, by which, weak classifier is able to reject background more efficiently. Then, a coarse-to-fine scan is applied. Coarse scan is used to find possible face location, and the fine scan refines the face location and rejects false alarms. We further improve the speed of multi-scale face detection by introducing two different template sizes for detector training. By which, the smaller faces can be rapidly detected and the high performance is kept for larger faces. The proposed method is evaluated on public dataset FDDB, the result shows competitive performance against all Adaboost based methods. The method has been implemented on mobile phone and the speed is superior to all competitors.

Stereo rectification removes vertical disparity between stereo image pair and it can improve visual quality of stereoscopic displays. This work addresses a practical issue when a stereo video input has been enhanced with post inserted objects (PIOs) such as captions and graphic objects. In such cases, previous stereo rectification methods would result in errors in the PIO regions due to PIOs' artificially edited epipolar geometry than those of the scene objects. To avoid such problem, this work proposes a PIO calibration method, which involves extracting PIOs, filling the PIO pixels, and re-inserting the PIOs into the rectified stereo video. The method constructs PIO models to extract PIOs in real stereo videos. Also, a modified stereo rectification procedure is proposed to work with the PIO calibration method, which reduces computational load and avoids video jitter artifacts. The method is tested on both real and simulated stereo videos to verify its validity.

Saliency detection for images and videos has become increasingly popular due to its wide applicability. In this paper, we propose a novel bottom-up video saliency extraction method, which includes two parts: static saliency detection and dynamic saliency detection. Firstly, we consider the static saliency detection as a classification problem: a scene can be divided to saliency region and non-saliency region. To solve such problem, we use a frequency-based sampling method and then introduce a Canonical Correlation Analysis (CCA)-based classification strategy using multiple cues. Secondly, we combine motion feature with the spatial feature to represent dynamic saliency in temporal domain. We test our method in both image based saliency extraction and video saliency extraction. The results illustrate its high performance in both two tasks.

In this paper, we propose an exact and efficient O(1) edge-aware filter. Starting from the generic 1D weighted average filter, by enforcing two assumptions on the filter kernel, we derive the fast O(1) algorithm to exactly implement the filter by two pass recursions. Performing the 1D filter horizontally and vertically can easily extend it to 2D case. A typical real valued geodesic kernel and a novel complex valued bilateral-like kernel are investigated in our proposed filtering framework. Experiments show that both kernels work well for the edge-aware smoothing task, and the bilateral-like kernel based filter shows the similar ability as the guided filter in the image feathering application.

In this paper, we propose a precise and fast eye center localization method. The method is based on SDM [1] algorithm, which has achieved impressive performance in face alignment area. In this work, we further improve the basic SDM algorithm in two aspects: 1) employ the coarse to fine strategy to enhance the robustness to initialization; 2) utilize multiple nonlinear features to ensure the robustness while improve the accuracy. Experimental results on BioID database shows that our improved SDM achieves preferable localization accuracy against the state-of-the-art methods at a speed as fast as 2ms to locate two eyes.

Existing appearance-based gaze estimation methods suffer from tedious calibration and appearance variation caused by head movement. In this paper, to handle this problem, we propose a novel appearance-based gaze estimation method by introducing supervised adaptive feature extraction and hierarchical mapping model. Firstly, an adaptive feature learning method is proposed to extract topology-preserving (TOP) feature individually. Then hierarchical mapping method is proposed to localize gaze position based on coarse-to-fine strategy. Appearance synthesis approach is used to increase the refer sample density. Experiments show that under the condition of sparse calibration, proposed method has better performance in accuracy than existing methods under fixed head pose without chinrest. Moreover, our method can be easily extended for head pose-varying gaze estimation.

Integral imaging display (IID) is a promising technology to provide realistic 3D image without glasses. To achieve a large screen IID with a reasonable fabrication cost, a potential solution is a tiled-lens-array IID (TLA-IID). However, TLA-IIDs are subject to 3D image artifacts when there are even slight misalignments between the lens arrays. This work aims at compensating these artifacts by calibrating the lens array poses with a camera and including them in a ray model used for rendering the 3D image. Since the lens arrays are transparent, this task is challenging for traditional calibration methods. In this paper, we propose a novel calibration method based on defining a set of principle observation rays that pass lens centers of the TLA and the camera's optical center. The method is able to determine the lens array poses with only one camera at an arbitrary unknown position without using any additional markers. The principle observation rays are automatically extracted using a structured light based method from a dense correspondence map between the displayed and captured pixels. Experiments show that lens array misalignments can be estimated with a standard deviation smaller than 0.4 pixels. Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.

This paper presents a novel tree-based cost aggregation method for dense stereo matching. Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, "Segment-Tree", is proposed for non-local matching cost aggregation. Conceptually, the segment-tree is constructed in a three-step process: first, the pixels are grouped into a set of segments with the reference color or intensity image, second, a tree graph is created for each segment, and in the final step, these independent segment graphs are linked to form the segment-tree structure. In practice, this tree can be efficiently built in time nearly linear to the number of the image pixels. Compared to MST where the graph connectivity is determined with local edge weights, our method introduces some 'non-local' decision rules: the pixels in one perceptually consistent segment are more likely to share similar disparities, and therefore their connectivity within the segment should be first enforced in the tree construction process. The matching costs are then aggregated over the tree within two passes. Performance evaluation on 19 Middlebury data sets shows that the proposed method is comparable to previous state-of-the-art aggregation methods in disparity accuracy and processing speed. Furthermore, the tree structure can be refined with the estimated disparities, which leads to consistent scene segmentation and significantly better aggregation results.

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.