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DeepFace: A High-Precision and Scalable Deep Learning Pipeline for Predicting Large-Scale Brain Activity from Facial
Kemal Ozdemirli1,2, Tenesha Connor1,3, Kaleb Kim1,4
1Department of Neuroscience, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA.
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We present DeepFace, a next-generation facial analysis pipeline that enhances orofacial tracking and cortical activity prediction in mice. Rather than replacing existing tools, DeepFace builds upon DeepLabCut and Facemap to address scalability bottlenecks and improve behavioral quantification. It offers high precision, keypoint customization, and robust performance across GCaMP6s, GCaMP6f, and jGCaMP8m lines. With scalable batch processing and high-performance computing compatibility, DeepFace enables high-throughput brain-behavior analysis in large-scale preclinical neuroscience.
