## 1 Introduction
Heavy models are not suitable for these platforms due to their inference latency and energy consumption. In contrast, lightweight models can perform real-time inference directly on edge devices, reducing latency, enhancing user privacy by avoiding cloud transmission, and lowering bandwidth requirements. Moreover, many practical FER applications, such as driver drowsiness detection, classroom engagement monitoring, and mobile mental health assessment, require continuous, long-term operation. Lightweight models consume significantly less power, making them ideal for battery-powered or energy-sensitive scenarios. They also facilitate large-scale deployment in scenarios such as intelligent surveillance or retail analytics, where hundreds of devices may need to operate simultaneously with minimal hardware cost. Lightweight FER models also contribute positively to model generalizability and ethical deployment. By design, these models often have fewer parameters and simpler architectures, which may help mitigate overfitting, especially in scenarios with limited or imbalanced training data. Furthermore, performing emotion recognition locally on-device alleviates privacy concerns, aligning with data protection regulations such as GDPR and addressing growing public scrutiny of biometric technologies. In summary, designing lightweight FER models is essential for bridging the gap between academic research and real-world applications. These models provide a feasible and scalable solution for deploying emotion recognition in practical, resource-constrained environments without sacrificing performance, making them a cornerstone of future FER systems. Therefore, numerous researchers have begun to design new lightweight convolutional neural networks. The SqueezeNet proposed by [PERSON] [24] pioneered the development of lightweight convolutional neural networks. This model is composed of several FireModels combined with convolutional layers, fully connected layers, etc. The FireModel includes two parts, squeeze and expand, which reduce the number of parameters by first reducing and then increasing the dimensionality of the feature map. In 2017, Google proposed the MobileNet [25] model, which uses a depthwise separable convolution to replace traditional convolution operations. Depthwise separable convolution decomposes the traditional convolution layer into two steps, namely, deep convolution first and then point wise convolution. Because MobileNet uses a large number of 3 \(\times\) 3 convolution kernels, using depthwise separable convolution instead of traditional convolution operations can reduce the computational load by about nine times. At the same time, MobileNet also provides a hyperparameter to control the width of the network, balancing the relationship between model accuracy and model size. Following this, Google proposed the Xception [26] model, which is based on the idea of the Inception model and uses depthwise separable convolutions to replace the Inception module, and introduces residual structures. In 2018, the Google team proposed the MobileNetV2 [27] model. Compared to the MobileNet model, MobileNetV2 introduces an inverted residual structure, which ascends and then descends the dimensions to enhance the propagation of gradients, and removes the ReLU activation function for the second pointwise convolution in the inverted residual module, preserving the diversity of features. ShuffleNet [28], proposed by Kuangxiang Technology, uses point grouping convolution instead of point convolution operation to reduce the amount of computation, and at the same time proposes a channel shuffle method to solve the problem of not being able to exchange information between different groups due to the use of grouping convolution. The ShuffleNetV2 [29] model proposed by [PERSON] et al.