Recently, WIDER FACE, one of the world’s authoritative public evaluation dataset for face detection, released its latest rankings. “AInnoFace”, the algorithm developed by AInnovation surpassed the performance of many well-known AI enterprises and university laboratories, including Baidu, Tencent, JD.com, DiDi, Carnegie Mellon, University of Chinese Academy of Sciences, among others. AInnoFace ranked first overall across all three subsets “Easy”, “Medium” and “Hard” of the WIDER FACE Challenge under six evaluation criteria (ranking first in five criteria and second in only one).
Taking the “World’s Largest Selfie” for example, the AInnoFace algorithm detected 918 faces with a high degree of detection accuracy. By comparison, Baidu’s PyramidBox algorithm detected only 880 faces in the photo.
The AInnoFace algorithm of AInnovation is developed from the well-known RetinaNet single-stage detector, and adopts a IoU regression loss for Bounding-Box regression to improve location detection accuracy, implements a Selective Refinement Network for higher Recall Rates and lower False Positive Rates, utilizes an augmented strategy of rich data variety for more robust final detection model, leverages Max-out Label to make more accurate classification predictions, and uses an improved multi-scale detection strategy to detect better faces at different scales. Through the above mentioned improvements, the AInnoFace algorithm can maintain outstanding detection performance even at the extremes, if faces are small, dimmed or covered. Altogether, the AInnoFace algorithm is able to effectively improve the recall rate and accuracy of face detection in complex scenes, and addresses the technical problems of face detection in public scenarios.