# Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification
## 2 Related Work
### Classification Model  
For hand recognition, MobileNetV2--with its lightweight architecture and fast inference speed--ResNet-50--based on residual learning--and Inception V3--which leverages various filter sizes for improved image recognition--have been widely used. First, MobileNetV2 adopts a streamlined architecture based on standard operations, enabling both memory-efficient and computationally efficient inference for mobile applications. This model demonstrates superior performance on benchmark datasets such as ImageNet and COCO, achieving a favorable balance between accuracy and complexity. In combination with the SSDLite detection module, MobileNetV2 requires 20% fewer computations and 10% fewer parameters compared to YOLOv2. The network structure also separates model expressiveness--through expansion layers--from capacity--via bottleneck inputs--facilitating effective model scaling [27; 28]. [PERSON] et al. [29] proposed a palm vein image recognition approach based on MobileNetV2, incorporating transfer learning and SENet modules to improve robustness and accuracy under conditions such as lighting variation and limited data. By segmenting a region of interest (ROI) rich in vein patterns and enhancing critical feature representation through SENet, their image recognition model achieved a high accuracy of 99.35%. [PERSON] et al. [30] developed a sign language recognition system for individuals with hearing and speech impairments using a MobileNetV2-based model. They trained the model on a dataset comprising 24 alphabet gesture classes (excluding J and Z, which require motion), achieving 99.9% validation accuracy and 100% test accuracy. Next, ResNet-50 enables the effective training of deep neural networks with up to 50 layers, making it well-suited for image recognition by achieving higher accuracy compared to low-depth networks. Residual connections mitigate the vanishing gradient problem and facilitate the training of deeper models, effectively addressing challenges in object recognition [31; 32]. [PERSON] and [PERSON] [32] applied the ResNet50 model to object detection tasks using the COCO dataset and demonstrated its robustness in identifying and localizingobjects under complex conditions. Their results showed that ResNet50 achieved higher accuracy compared to other lightweight models like MobileNet and EfficientNet, particularly in scenarios with occlusion and scale variation. [PERSON] et al. (2024) compared the performance of ResNet18, ResNet34, ResNet50, and ResNet101 models for hand recognition using palm and dorsal hand images. Among these, ResNet50 achieved the highest accuracy (92.5%) and the lowest loss rate (0.2372%) [33]. [PERSON] [34] proposed a static hand gesture recognition system based on the ResNet50 architecture, enabling control of a web browser through gesture input. The proposed system was constructed by fine-tuning a pre-trained ResNet50 model on a target gesture dataset and achieved an average recognition accuracy of 93.86% on the test set. To demonstrate its practical applicability, eight static gestures were predefined and mapped to web browser commands (e.g., open browser, switch page), enabling gesture-based control of browsing functions. Lastly, Inception V3 is a high-performance convolutional neural network (CNN) architecture designed to balance computational efficiency and accuracy, employing structural optimizations such as factorized convolutions, aggressive dimension reduction, auxiliary classifiers, and label-smoothing to achieve strong performance with relatively low computational cost and parameter count [35]. Additionally, the Inception-v3 architecture employs \(1\times 1\) convolutions (pointwise convolutions) in conjunction with parallel convolutional layers of varying kernel sizes, along with an increased depth through additional hidden layers. This design enhances the model's ability to capture complex and abstract features, making it especially effective for addressing sophisticated recognition tasks [36]. [PERSON] et al.