Spiking Neural Network Emotion Recognition Model for Low Energy Video Processing
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Solution Overview
Problem
Current emotion recognition methods, particularly using Artificial Neural Networks (ANNs), face challenges in energy consumption, limiting their application in embedded and mobile devices, and struggle to extract emotion information from video clips effectively.
Innovation Solution
A method utilizing Spiking Neural Networks (SNNs) is developed to recognize emotions based on video information, where spiking sequences are acquired and recognized using a trained SNN emotion recognition model, incorporating a dynamic visual data set and simulation processing to convert raw visual data into spiking sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Artificial Neural Network (ANN) is used for emotion recognition, then emotion recognition accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent replaces the traditional Artificial Neural Network (ANN) mechanical computing system with a Spiking Neural Network (SNN) that mimics biological neuron behavior. The SNN uses event-driven spiking mechanisms where neurons only activate when receiving sufficient input, significantly reducing unnecessary computations and energy consumption while maintaining emotion recognition accuracy. This substitution transforms the continuous activation model of ANN into a discrete, biologically-inspired spiking model that consumes less energy.
Solution Approach 2:
The patent changes the fundamental operating parameters of the neural network by transitioning from continuous activation functions in ANN to discrete spike timing and frequency coding in SNN. The emotion recognition model uses spiking sequences with varying temporal patterns to represent different emotional states, allowing the system to maintain high recognition accuracy while operating with lower energy consumption through sparse, event-driven computation.
2Use of energy by moving object
If Spiking Neural Network (SNN) is used for emotion recognition, then energy consumption is reduced, but ability to process video information is limited
Solution Approach 1:
The patent creates a universal SNN-based emotion recognition system that can process multiple types of input data including video clips, audio signals, and physiological data. The spiking neural network architecture is designed with modular components that can accept different input modalities and integrate them through spike timing-dependent plasticity mechanisms, enabling the system to handle video information effectively while maintaining low energy consumption characteristics.
Solution Approach 2:
The patent enhances the SNN's video processing capability by introducing temporal dimension through spiking sequences. Instead of processing static images, the system processes sequences of video frames encoded as temporal patterns of spikes, where the timing and frequency of spikes carry emotional information. This dimensional transformation allows the energy-efficient SNN to effectively process dynamic video information by leveraging temporal coding strategies.
3Speed
If traditional emotion recognition methods are used, then processing speed is maintained, but application in embedded and mobile devices is hindered
Solution Approach 1:
The patent replaces traditional ANN-based processing systems with SNN architecture that is inherently more suitable for embedded and mobile devices. The event-driven nature of SNN allows it to operate efficiently on resource-constrained hardware by activating only when necessary, reducing the computational burden on mobile processors while maintaining real-time processing capabilities through asynchronous spike-based communication between neurons.
Data Source
AI summary
A method for recognizing emotion, a training method, apparatuses, a device, a storage medium and a product. The method for recognizing emotion includes: acquiring to-be-recognized spiking sequences corresponding to video information; and recognizing the to-be-recognized spiking sequences by using a spiking neural network emotion recognition model, so as to obtain a corresponding emotion category.