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Hybrid Cascade Model for Face Detection in the Wild Based on Normalized Pixel Difference and a Deep Convolutional Neural Network


Marčetić, Darijan; Soldić, Martin; Ribarić, Slobodan
Hybrid Cascade Model for Face Detection in the Wild Based on Normalized Pixel Difference and a Deep Convolutional Neural Network // Lecture notes in computer science, 10425 (2017), 379-390 doi:10.1007/978-3-319-64698-5_32 (međunarodna recenzija, članak, znanstveni)


Naslov
Hybrid Cascade Model for Face Detection in the Wild Based on Normalized Pixel Difference and a Deep Convolutional Neural Network

Autori
Marčetić, Darijan ; Soldić, Martin ; Ribarić, Slobodan

Izvornik
Lecture notes in computer science (0302-9743) 10425 (2017); 379-390

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Face Detection in the Wild ; Normalized Pixel Difference Model ; Deep Convolutional Neural Networks

Sažetak
The main precondition for applications such as face recognition and face de-identification for privacy protection is efficient face detection in real scenes. In this paper, we propose a hybrid cascade model for face detection in the wild. The cascaded two-stage model is based on the fast normalized pixel difference (NPD) detector at the first stage, and a deep convolutional neural network (CNN) at the second stage. The outputs of the NPD detector are characterized by a very small number of false negative (FN) and a much higher number of false positive face (FP) detections. The FP detections are typically an order of magnitude higher than the FN ones. This very high number of FPs has a negative impact on recognition and/or de-identification processing time and on the naturalness of the de-identified images. To reduce the large number of FP face detections, a CNN is used at the second stage. The CNN is applied only on vague face region candidates obtained by the NPD detector that have an NPD score in the interval between two experimentally determined thresholds. The experimental results on the Annotated Faces in the Wild (AFW) test set and the Face Detection Dataset and Benchmark (FDDB) show that the hybrid cascade model significantly reduces the number of FP detections while the number of FN detections are only slightly increased.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

Napomena
Computer Analysis of Images and Patterns : 17th International Conference (CAIP 2017) Ystad, Švedska, August 22.-24.08.2017.g., Proceedings, Part II. Editors: Felsberg, Michael, Heyden, Anders, Krüger, Norbert (Eds.)



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2013-11-6733 - Zaštita privatnosti deidentifikacijom u nadzornim sustavima (Slobodan Ribarić, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Citiraj ovu publikaciju

Marčetić, Darijan; Soldić, Martin; Ribarić, Slobodan
Hybrid Cascade Model for Face Detection in the Wild Based on Normalized Pixel Difference and a Deep Convolutional Neural Network // Lecture notes in computer science, 10425 (2017), 379-390 doi:10.1007/978-3-319-64698-5_32 (međunarodna recenzija, članak, znanstveni)
Marčetić, D., Soldić, M. & Ribarić, S. (2017) Hybrid Cascade Model for Face Detection in the Wild Based on Normalized Pixel Difference and a Deep Convolutional Neural Network. Lecture notes in computer science, 10425, 379-390 doi:10.1007/978-3-319-64698-5_32.
@article{article, year = {2017}, pages = {379-390}, DOI = {10.1007/978-3-319-64698-5\_32}, keywords = {Face Detection in the Wild, Normalized Pixel Difference Model, Deep Convolutional Neural Networks}, journal = {Lecture notes in computer science}, doi = {10.1007/978-3-319-64698-5\_32}, volume = {10425}, issn = {0302-9743}, title = {Hybrid Cascade Model for Face Detection in the Wild Based on Normalized Pixel Difference and a Deep Convolutional Neural Network}, keyword = {Face Detection in the Wild, Normalized Pixel Difference Model, Deep Convolutional Neural Networks} }

Časopis indeksira:


  • Scopus


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