Abstract: [Objective] To address the issues of high invasiveness, low efficiency, and susceptibility of chips to frequency interference in traditional individual horse identification methods, an open-set individual horse face recognition method based on multi-scale feature fusion was explored. [Methods] Selecting MobileFaceNet as the backbone network, a lightweight horse face recognition network, HorseFaceNet, was designed by incorporating an Enhanced Bidirectional Feature Pyramid Network （EnhancedBiFPN） module to achieve multi-scale feature fusion. Using a self-built Ili horse facial image dataset, training was conducted on the training set with partial classes selected via five independent random resamplings. Testing was performed on a test set containing all classes, followed by calculation of the mean recognition accuracy. [Results] To verify the robustness and generalization ability of the model in open-set scenarios, the proposed HorseFaceNet was systematically evaluated under training settings with different proportions of known classes. Experimental results demonstrated that the model achieved a mean accuracy of 98.28% when the training set included 70% of known classes. When the proportion of known classes decreased to 50%, the mean accuracy reached 97.28%. Even with only 30% of known classes used for training, the model maintained a mean accuracy of 95.52%. With a parameter size of only 1.72 M, the proposed HorseFaceNet reduced the number of parameters by approximately 0.39 M （about 18.5%） compared to the original MobileFaceNet, while improving the recognition accuracy by 3.09 percentage points in an open-set recognition scenario with 50% of classes participating in training. [Conclusion] These results fully indicate that the HorseFaceNet model possesses excellent recognition performance and strong generalization ability even under conditions of limited samples from known classes. The model balances model lightweight with improved recognition performance, making it of wide application value for intelligent horse farm management and other practical scenarios.

Key words: MobileFaceNet, Bidirectional Feature Pyramid Network （BiFPN）, open-set recognition, smart ranch, horse face recognition, multi-scale feature fusion