The st UTS-VNU Research School Advanced Technologies for IoT Applications Deep Convolutional Neural Network in Deformable Part Models for Face Detection Nguyen Dinh Luan, University of Science, VNU-HCMC, Ho Chi Minh city, Vietnam Abstract Deformable Part Models (DPM) [1] and Convolutional Neural Network (CNN) are state-of-the-art approaches in object detection While DPM makes use of the general structure between parts and root models, CNN uses all information of input to create meaningful features These two types of characteristics are necessary for face detection Experimental results show that our method surpasses the highest result of existing methods for face detection on the standard dataset with 87.06% in true positive rate at 1000 number false positive images Our method sheds a light in face detection which is commonly regarded as a saturated area Introduction Contributions Face detection is not a new area BUT challenging: - The variation of face’s pose, lighting conditions and occlusion of images in the wild - Does not solve completely with high precision and speed There are two key ideas - new 4-5 part Face DPM model for face detection - new adaptive way of integrated CNN into DPM called DeepFace DPM What is DPM? a model use: HOG + latent SVM + provides parts and structure of object - does not exploit high level features What is CNN? a type of learning with layers + meaningful features - does not provide explicit relationship between features Results Dataset: Face Detection Data Set and Benchmark (FDDB) [2] : contains 5171 faces in 2845 images with variation of background, illumination, face’s pose and appearance Results of state-of-the-art methods are published in FDDB’s website Evaluation: We use standard evaluation protocol provided with dataset Fig Superiority of proposed method First row: results detected by DPM Second row: results detected by CNN Third row: results detected by our method Fig Comparison with state-of-the-art techniques on FDDB dataset Conclusion References Our system reveals the fact that : [1] Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models Pattern Analysis and Machine Intelligence, IEEE Transactions on 32 (2010) [2] Jain, V., Learned-Miller, E.G.: Fddb: A benchmark for face detection in unconstrained set-tings UMass Amherst Technical Report (2010) structure learning and deep learning can be integrated together to get the top performance DeepFaceDPM becomes new state-of-the-art in Face detection area