Today’s development focuses on the integration of affective meaning into products and services. Kansei Engineering (KE) is a technology that establishes the relationship between customers’ kansei and design elements to optimize the design for customers. This study aims to identify the research status and emerging trends in KE research by using visualization analysis with CiteSpace. We retrieve 2830 articles from the core collection of the Web of Science (1995–2021). First, based on the chronological distribution and background of research institutions, journals, literatures, countries and core authors, this study analyzes the main contributions, knowledge sources, interdisciplinary characteristics and major research domains of KE. Second, the research hotspots and cutting-edge technologies are intuitively presented and analyzed to reveal the knowledge structure and research frontier. Finally, this research expounds upon the evolution and developmental process of each stage and proposes the emerging trends of KE research, which are the use of big data in the Internet era, multidisciplinary cooperation, and the integration of multiple mathematical algorithms to serve multiapplication practice. This is also the orientation for further research on KE in the succeeding stages.

Spontaneous speech emotion recognition is a new and challenging research topic. In this paper, we propose a new method of spontaneous speech emotion recognition on the basis of binaural representations and deep convolutional neural networks (CNNs). The proposed method initially employs multiple CNNs to learn deep segment-level binaural representations such as Left-Right and Mid-Side pairs from the extracted image-like Mel-spectrograms. These CNNs are fine-tuned on target emotional speech datasets from a pre-trained image CNN model. Then, a new feature pooling strategy, called block-based temporal feature pooling, is proposed to aggregate the learned segment-level features for producing fixedlength utterance-level features. Based on the utterance-level features, linear support vector machines (SVM) is adopted for emotion classification. Finally, a two-stage score-level fusion strategy is used to integrate the obtained results from Left-Right and Mid-Side pairs. Extensive experiments on two challenging spontaneous emotional speech datasets, including the AFEW5.0 and BAUM-1s databases, demonstrate the effectiveness of our proposed method.

Spiking neural networks (SNN) represent a paradigm shift toward discrete, event-driven neural computation that mirrors biological brain mechanisms. This survey systematically examines current SNN research, focusing on training methodologies, hardware implementations, and practical applications. We analyze four major training paradigms: ANN-to-SNN conversion, direct gradient-based training, spike-timing-dependent plasticity (STDP), and hybrid approaches. Our review encompasses major specialized hardware platforms: Intel Loihi, IBM TrueNorth, SpiNNaker, and BrainScaleS, analyzing their capabilities and constraints. We survey applications spanning computer vision, robotics, edge computing, and brain-computer interfaces, identifying where SNN provide compelling advantages. Our comparative analysis reveals SNN offer significant energy efficiency improvements (1 000–10 000× reduction) and natural temporal processing, while facing challenges in scalability and training complexity. We identify critical research directions including improved gradient estimation, standardized benchmarking protocols, and hardware-software co-design approaches. This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities, limitations, and future prospects.

The proposed method has 30 streams, i.e., 15 spatial streams and 15 temporal streams. Each spatial stream corresponds to each temporal stream. Therefore, this work correlates with the symmetry concept. It is a difficult task to classify video-based facial expression owing to the gap between the visual descriptors and the emotions. In order to bridge the gap, a new video descriptor for facial expression recognition is presented to aggregate spatial and temporal convolutional features across the entire extent of a video. The designed framework integrates a state-of-the-art 30 stream and has a trainable spatial–temporal feature aggregation layer. This framework is end-to-end trainable for video-based facial expression recognition. Thus, this framework can effectively avoid overfitting to the limited emotional video datasets, and the trainable strategy can learn to better represent an entire video. The different schemas for pooling spatial–temporal features are investigated, and the spatial and temporal streams are best aggregated by utilizing the proposed method. The extensive experiments on two public databases, BAUM-1s and eNTERFACE05, show that this framework has promising performance and outperforms the state-of-the-art strategies.

It’s a challenging task to recognize facial expression in video sequences due to the gap between the hand-crafted features and the subjective emotions. To bridge the gap, this paper proposes a novel method of video-based facial expression recognition using deep temporal–spatial networks. The proposed method firstly employs multimodal deep convolutional neural networks (CNN), including the spatial CNN network and the temporal CNN network, to extract high-level spatial and temporal features in video sequences, respectively. The temporal–spatial CNN networks are fine-tuned on target video facial expression data from a pre-trained CNN model. Specially, the spatial network is used to learn deep spatial features from the static expression images in a video. Likewise, the temporal network is adopted to learn deep temporal features from the produced optical flow images between multiple frames in a video. Then the extracted spatial and temporal features are combined in a fusion network to conduct video-based facial expression classification tasks. Extensive experiments on two public video-based facial expression datasets, i.e. the BAUM-1s and RML database, demonstrate the promising performance of the proposed method, outperforming the-state-of-the-arts.