Marginal Fisher Analysis (MFA) is a classical dimensionality reduction (DR) method that leverages dual graphs to capture intra-class compactness and inter-class separability. However, MFA’s reliance on high-quality labels limits its practical application. For another, existing unsupervised DR methods neglect data’s local manifold relationship, resulting in poor discriminativeness. To address these limitations, we propose a novel DR method named Discriminative Graph Embedding Framework (DGEF) via Label-Free Marginal Fisher Analysis. Our approach uses the adjacency matrix and cluster indicator matrix derived from centerless K-Means to construct intrinsic graph and penalty graph, which preserve the local manifold structure of the data. Additionally, we have derived the convertible relationship between centerless K-Means and Manifold learning and unified them within a graph embedding framework. By adopting the intrinsic graph and penalty graph, our DGEF avoids centroid initialization and ensures robustness and discriminativeness. This method achieves dimensionality reduction adaptively without relying on labeled data. Extensive experiments on benchmark datasets show that our approach outperforms conventional methods in clustering performance.

Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with background noise, leading to poor performance. In this study, a cascaded Incremental Region Proposal Network (Inc-RPN) is proposed to accurately detect apple leaf diseases in natural environments. The proposed Inc-RPN has a two-layer RPN architecture, where the precursor RPN is leveraged to generate diseased leaf proposals, and the successor RPN focuses on extracting target disease spots based on diseased leaf proposals. In the successor RPN, a low-level feature aggregation module is designed to fully utilize the bridged features and preserve the semantic information of the target disease spots. An incremental module is also leveraged to extract aggregated diseased leaf features and target disease spot features. Finally, a novel position anchor generator is designed to generate anchors based on diseased leaf proposals. The experimental results show that the proposed Inc-RPN performs very well on the FALD_CED and Apple Leaf Disease datasets, showing that it can accurately perform apple leaf disease detection tasks.

Apple leaf diseases seriously affect the quality of apples and may lead to yield losses, detecting apple leaf diseases accurately can prevent diseases from spreading and promote the healthy growth of the industry. However, recent studies cannot achieve accurate detection of leaf diseases with high accuracy because the lesions are of different sizes. So, this paper proposed a novel apple leaf disease detection method called VMF-SSD (V-space-based Multi-scale Feature-fusion SSD), which is designed to extract more reliable multi-scale feature representations for varied sizes of diseased spots and improve the final detection performance. The multi-scale feature extraction is established with multi-scale feature representation to further improve the disease detection performance, especially for small spots. After that, a V-space-based location branch is presented to enhance the texture feature information and help further identify disease spot location. Finally, attention mechanisms are utilized to automatically learn the importance of feature channels at different scales for distinguishing diseased spots of different sizes. Experimental results showed that the VMF-SSD method achieves 83.19% mAP and obtains the detection speed of 27.53 FPS on the test set, which indicates that the proposed VMF-SSD method can achieve competitive performance on apple leaf diseases detection task and satisfy the requirements of agricultural production applications.

Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals. This paper proposes a novel and real-time early apple leaf disease recognition model. AD Convolution is firstly utilized to replace standard convolution to make smaller number of parameters and calculations. Meanwhile, a LAD-Inception is built to enhance the ability of extracting multiscale features of different sizes of disease spots. Finally, the LAD-Net model is built by the LR-CBAM and the LAD-Inception modules, replacing a full connection with global average pooling to further reduce parameters. The results show that the LAD-Net, with a size of only 1.25MB, can achieve a recognition performance of 98.58%. Additionally, it is only delayed by 15.2ms on HUAWEI P40 and by 100.1ms on Jetson Nano, illustrating that the LAD-Net can accurately recognize early apple leaf pests and diseases on mobile devices in real-time, providing portable technical support.

Pain is a significant indicator that shows people are suffering from an unwell experience and its automatic estimation has attracted much interest in recent years. Of late, most estimation methods are designed to capture the dynamic pain information from visual signals while a few physiological-signal based methods can provide extra potential cues to analyze the pain more accurately. However, it is still challenging to capture the physiological data from patients as it requires contact devices and patients’ cooperation. In this paper, we propose to leverage the pseudo physiological information by generating new modal data from the original visual videos and jointly estimating the pain by an end-to-end network. To extract the representations from bi-modal data, we design a spatio-temporal pain estimation network, which employs a dual-branch framework for extracting pain-aware visual and pseudo physiological features separately and fuses the features in a probabilistic way. The inherent vital sign, i.e., heart rate gain (HRG), from pseudo physiological information can be utilized as an auxiliary signal and integrated with the visual pain estimation framework. Moreover, specially-designed 3D convolution filters and attention structures are employed to extract spatio-temporal features for both branches. To use the HRG as an auxiliary way for pain estimation, we propose a probabilistic inference model by jointly considering the visual branch and physiological branch, which makes our model estimate the pain comprehensively. Experiments on two publicly-available datasets show the effectiveness of introducing the pseudo modality, and the proposed method can outperform the state-of-the-art methods.

Similar to the basic facial expression recognition, one challenge for pain intensity recognition is some individual characteristics, e.g. face shapes, may cause great diversities in the same emotion. So it is usually very difficult to distinguish two adjacent intensity levels of pain expression as each intensity has a large variation. In this study, a coarse‐to‐fine combination method is proposed for pain intensity recognition. The results of multi‐scale outputs from multiple base deep network are combined in a probabilistic way for improving the discrimination between visually similar adjacent levels. A two‐layer tree classifier is proposed in a multi‐task framework for pain intensity recognition as well as face shape recognition, replacing the planar Softmax classifier in each base deep network. In the first layer of tree classifier, multi‐scale classifiers are constructed for recognizing facial pain intensities and the conventional classifiers are constructed for face shape recognition in the second layer. Finally, the tree classifier including multi‐scale classifiers and conventional classifiers is jointly optimised during the training phase and only high level classifiers are used for recognising pain intensities in the test phase. The extensive experiments on UNBC shoulder pain dataset show the proposed method gets promising results in pain intensity recognition.

Face presentation attacks are the main threats to face recognition systems, and many presentation attack detection (PAD) methods have been proposed in recent years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks. That is because the replayed fake faces contain a variety of aliveness signals, such as eye blinking and facial expression changes. Replayed video attacks occur when attackers try to invade biometric systems by presenting face videos in front of the cameras, and these videos are often launched by a liquid-crystal display (LCD) screen. Due to the smearing effects and movements of LCD, videos captured from the real and replayed fake faces present different motion blurs, which are reflected mainly in blur intensity variation and blur width. Based on these descriptions, a motion blur analysis-based method is proposed to deal with the replayed video attack problem. We first present a 1D convolutional neural network (CNN) for motion blur intensity variation description in the time domain, which consists of a serial of 1D convolutional and pooling filters. Then, a local similar pattern (LSP) feature is introduced to extract blur width. Finally, features extracted from 1D CNN and LSP are fused to detect the replayed video attacks. Extensive experiments on two standard face PAD databases, i.e., relay-attack and OULU-NPU, indicate that our proposed method based on the motion blur analysis significantly outperforms the state-of-the-art methods and shows excellent generalization capability.