Trung Thanh Ngo

In this work, we propose a novel learning-based method to jointly estimate the shape and subsurface scattering (SSS) parameters of translucent objects by utilizing polarization cues. Although polarization cues have been used in various applications, such as shape from polarization (SfP), BRDF estimation, and reflection removal, their application in SSS estimation has not yet been explored. Our observations indicate that the SSS affects not only the light intensity but also the polarization signal. Hence, the polarization signal can provide additional cues for SSS estimation. We also introduce the first large-scale synthetic dataset of polarized translucent objects for training our model. Our method outperforms several baselines from the SfP and inverse rendering realms on both synthetic and real data, as demonstrated by qualitative and quantitative results.

We introduce a method of recovering the shape of a smooth dielectric object using diffuse polarization images taken with different directional light sources. We present two constraints on shading and polarization and use both in a single optimization scheme. This integration is motivated by photometric stereo and polarization-based methods having complementary abilities. Polarization gives strong cues for the surface orientation and refractive index, which are independent of the light direction. However, employing polarization leads to ambiguities in selecting between two ambiguous choices of the surface orientation, in the relationship between the refractive index and zenith angle (observing angle). Moreover, polarization-based methods for surface points with small zenith angles perform poorly owing to the weak polarization. In contrast, the photometric stereo method with multiple light sources disambiguates the surface normals and gives a strong relationship between surface normals and light directions. However, the method has limited performance for large zenith angles and refractive index estimation and faces strong ambiguity when light directions are unknown. Taking the advantages of these methods, our proposed method recovers surface normals for small and large zenith angles, light directions, and refractive indexes of the object. The proposed method is positively evaluated in simulations and real-world experiments.

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

Objective: The dual-task paradigm is a promising procedure for estimating cognitive status, and may also be collaterally used to reduce cognitive decline and prevent dementia. In this research, we use the Mini-Mental State Exam (MMSE) to assess cognitive status in the elderly as a reference, and investigate a potential of using machine learning for early detecting cognitive impairment in elderly. Methods: Although many studies have suggested that dual-task performance, in which participants perform a cognitive task while walking, is associated with cognition, they only considered the correlation between cognitive parameters and simple gait feature, such as gait speed, through statistical analysis. We instead use a Kinect sensor to capture participants’ whole-body movements and extract a rich gait feature that has the ability to exhibit different tendencies of movements between healthy and cognitive-impaired elderlies. Results: In our experiments, a classifier based on the dual-task gait feature achieved higher performance than the one based on single-task feature; the performance of the rich gait feature was better than that of a simple one; and an optimal detection performance was achieved with a MMSE cut-off score of 25. Conclusion: We positively validated that the proposed method could early detect elderly with lower MMSE scores based on dual-task gait feature with a promising performance. Significance: Our approach can support an early and automated diagnosis of cognitive impairment.

A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.