Among the members of biometric identifiers, the palmprint and the palmvein have received significant attention due to their stability, uniqueness, and non-intrusiveness. In this paper, we investigate the problem of palmprint/palmvein recognition and propose a Deep Convolutional Neural Network (DCNN) based scheme, namely $Palm{R}_{\mathrm{CNN}}$ (short for palmprint/palmvein recognition using CNNs). The effectiveness and efficiency of $Palm{R}_{\mathrm{CNN}}$ have been verified through extensive experiments conducted on benchmark datasets. In addition, though substantial effort has been devoted to palmvein recognition, it is still quite difficult for the researchers to know the potential discriminating capability of the contactless palmvein. One of the root reasons is that a large-scale and publicly available dataset comprising high-quality, contactless palmvein images is still lacking. To this end, a user-friendly acquisition device for collecting high quality contactless palmvein images is at first designed and developed in this work. Then, a large-scale palmvein image dataset is established, comprising 12,000 images acquired from 600 different palms in two separate collection sessions. The collected dataset now is publicly available.

Depression is a global mental health problem, the worst cases of which can lead to self-injury or suicide. An automatic depression detection system is of great help in facilitating clinical diagnosis and early intervention of depression. In this work, we propose a new automatic depression detection method utilizing speech signals and linguistic content from patient interviews. Specifically, the proposed method consists of three components, which include a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention layer to deal with linguistic content, a One-Dimensional Convolutional Neural Network (1D CNN) to deal with speech signals, and a fully connected network integrating the outputs of the previous two models to assess the depressive state. Evaluated on two publicly available datasets, our method achieves state-of-the-art performance compared with the existing methods. In addition, our method utilizes audio and text features simultaneously. Therefore, it can get rid of the misleading information provided by the patients. As a conclusion, our method can automatically evaluate the depression state and does not require an expert to conduct the psychological evaluation on site. Our method greatly improves the detection accuracy, as well as the efficiency.

Simultaneous localization and mapping via LiDAR-Inertial fusion is a crucial technology in many automation-related applications. Recently, a number of approaches based on geometric features have evolved, yielding impressive results via tightly-coupled estimation. This sort of feature-based techniques, however, are inextricably linked to the scanning mechanism of the LiDAR, relying on stable feature detection, and thus are difficult to adapt to multi-LiDAR systems. A few direct solutions, on the other hand, register the raw point cloud with the built probability map, which is more computationally efficient and easy to be extended. But, the existing direct approaches are all loosely-coupled, lacking correction of the IMU biases, and thus only work well in 2D cases. To this end, we present D-LIOM, a tightly-coupled Direct LiDAR-Inertial Odometry and Mapping framework. In D-LIOM, a scan is directly registered to a probability submap, and the LiDAR odometry, the IMU pre-integration, and the gravity constraint are integrated to build a local factor graph in the submap's time window, allowing the system to perform real-time high-precision pose estimation. Furthermore, to eliminate accumulated errors in time, we detect loops and adjust the sparse pose graph based on mutual matching of projected 2D submaps, allowing D-LIOM to run stably in large-scale scenes. In addition, to improve its flexibility to varied sensor combinations, D-LIOM supports multi-LiDAR inputs and facilitates the initialization with a common 6-axis IMU. Extensive experiments demonstrate that D-LIOM largely outperforms the existing state-of-the-art counterparts in mapping effect and localization accuracy as well as with high time efficiency. Lastly, to ensure that our results are entirely reproducible, all necessary data and codes are made open-source available. One introduction video can also be found on the online website.

Single image super-resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and generative adversarial networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with human vision system (HVS), we design a quality loss by integrating an image quality assessment (IQA) metric named gradient magnitude similarity deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variant of GANs named improved training of Wasserstein GANs (WGAN-GP). Besides GMGAN, we highlight the importance of training datasets. Experiments show that GMGAN with quality loss and WGAN-GP can generate visually appealing results and set a new state of the art. In addition, large quantity of high-quality training images with rich textures can benefit the results.

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.

The semantic SLAM (Simultaneous Localization And Mapping) system is a crucial module for autonomous indoor parking. Visual cameras (monocular/binocular) and IMU (Inertial Measurement Unit) constitute the basic configuration to build such a system. The performance of existing SLAM systems typically deteriorates in the presence of dynamically movable objects or objects with little texture. By contrast, semantic objects on the ground embody the most salient and stable features in the indoor parking environment. Due to their inabilities to perceive such features on the ground, existing SLAM systems are prone to tracking inconsistency during navigation. In this paper, we present MOFIS _SLAM , a novel tightly-coupled ${M}$ ulti- ${O}$ bject semantic SLAM system integrating ${F}$ ront-view, ${I}$ nertial, and ${S}$ urround-view sensors for autonomous indoor parking. The proposed system moves beyond existing semantic SLAM systems by complementing the sensor configuration with a surround-view system capturing images from a top-down viewpoint. In MOFIS _SLAM , apart from low-level visual features and inertial motion data, typical semantic objects (parking-slots, parking-slot IDs and speed bumps) detected in surround-views are also incorporated in optimization, forming robust surround-view constraints. Specifically, each surround-view feature imposes a surround-view constraint that can be split into a contact term and a registration term. The former pre-defines the position of each individual surround-view feature subject to whether it has semantic contact with other surround-view features. Three contact modes, defined as complementary , adjacent and coincident , are identified to guarantee a unified form of all contact terms. The latter further constrains by registering each surround-view observation and its position in the world coordinate system. In parallel, to objectively evaluate SLAM studies for autonomous indoor parking, a large-scale dataset with groundtruth trajectories is collected, which is the first of its kind. Its groundtruth trajectories, commonly unavailable, are obtained by tracking artificial features scattered in the indoor parking environment, whose 3D coordinates are measured with an ETS (Electronic Total Station). The collected dataset has been made publicly available at https://shaoxuan92.github.io/MOFIS .