Learning gender from human gaits and faces

Computer vision based gender classification is an important component in visual surveillance systems. In this paper, we investigate gender classification from human gaits in image sequences, a relatively understudied problem. Moreover, we propose to fuse gait and face for improved gender discrimination. We exploit Canonical Correlation Analysis (CCA), a powerful tool that is well suited for relating two sets of measurements, to fuse the two modalities at the feature level. Experiments demonstrate that our multimodal gender recognition system achieves the superior recognition performance of 97.2 % in large datasets. In this paper, we investigate gender classification from human gaits in image sequences using machine learning methods. Considering each modality, face or gait, in isolation has its inherent weakness and limitations, we further propose to fuse gait and face for improved gender discrimination. We exploit Canonical Correlation Analysis (CCA), a powerful tool that is well suited for relating two sets of signals, to fuse the two modalities at the feature level. Experiments on large dataset demonstrate that our multimodal gender recognition system achieves the superior recognition performance of 97.2%. We plot in Figure 1 the flow chart of our multimodal gender recognition system. 1

Pose-Normalized Image Generation for Person Re-identification

Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for synthesizing realistic person images conditional on the pose. The model is based on a generative adversarial network (GAN) designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN). With the synthesized images, we can learn a new type of deep re-id feature free of the influence of pose variations. We show that this feature is strong on its own and complementary to features learned with the original images. Importantly, under the transfer learning setting, we show that our model generalizes well to any new re-id dataset without the need for collecting any training data for model fine-tuning. The model thus has the potential to make re-id model truly scalable.Comment: 10 pages, 5 figure

Robust facial expression recognition using local binary patterns

A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple Local Binary Patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, whilst still retaining facial information efficiently. Template matching with weighted Chi square statistic and Support Vector Machine are adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade Database illustrate that the LBP features are effective and efficient for facial expression discrimination. Additionally, experiments on face images with different resolutions show that the LBP features are robust to low-resolution images, which is critical in real-world applications where only low-resolution video input is available. 1

European journal of dermatology : EJD

Vision-based human affect analysis is an interesting and challenging problem, impacting important applications in many areas. In this paper, beyond facial expressions, we investigate affective body gesture analysis in video sequences, a relatively understudied problem. Spatial-temporal features are exploited for modeling of body gestures. Moreover, we present to fuse facial expression and body gesture at the feature level using Canonical Correlation Analysis (CCA). By establishing the relationship between the two modalities, CCA derives a semantic “affect ” space. Experimental results demonstrate the effectiveness of our approaches.

Conditional Mutual Information Based Boosting for Facial Expression Recognition

This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. Low-cost LBP features are introduced to effectively describle local features of face images. A novel learning procedure, Conditional Mutual Information based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifiers that maximize their mutual information about a candidate class, conditional to the response of any weak classifier already selected; a strong classifier is constructed by combining the learned weak classifiers using the Naive-Bayes. Extensive experiments on the Cohn-Kanade database illustrated that LBP features are effective for expression analysis, and CMIB enables much faster training than AdaBoost, and yields a classifier of improved classification performance.

(Table 1) Concentrations of polybrominated diphenyl ethers in liver and plasma samples of ringed seals (Phoca hispida)

The present study investigated the concentrations and patterns of PBDEs and hydroxylated (OH) PBDE analogues in two ringed seal populations: less contaminated Svalbard and more contaminated Baltic Sea. Mean concentration of hepatic sum-PBDE, which was dominated by BDE47, was six times higher in the ringed seals from the Baltic Sea compared to the seals from Svalbard. BDE47/sum-PBDE was higher in the seals from Svalbard compared to that for Baltic seals, while the trend was opposite for BDE153 and 154. The geographical difference in contaminant pattern of PBDEs in ringed seals could be explained by biotransformation via oxidative metabolism and/or by dietary differences. OH-PBDEs were detectable in the majority of plasma samples from both locations, and dominated by bioaccumulation of naturally occurring congeners. Low levels of 3-OH-BDE47 and 4'-OH-BDE49 in the Baltic ringed seals suggested minor oxidative biotransformation of BDE47. In the Baltic seals, BDE153/sum-PBDEs and BDE154/sum-PBDEs increased and BDE28/sum-PBDE decreased with increasing sum-POP concentration, which suggests BDE153 and 154 are more persistent than BDE28. Contrasting diets of the ringed seals in these two locations may influence the PBDE congener pattern due to selective long-range transport and direct effluent emissions to Svalbard and the Baltic, respectively