Advances in Face Detection and Facial Image Analysis by Michal Kawulok, Emre Celebi, Bogdan Smolka

By Michal Kawulok, Emre Celebi, Bogdan Smolka

This publication offers the cutting-edge in face detection and research. It outlines new study instructions, together with particularly psychology-based facial dynamics reputation, aimed toward a number of purposes equivalent to habit research, deception detection, and prognosis of assorted mental problems. themes of curiosity contain face and facial landmark detection, face attractiveness, facial features and emotion research, facial dynamics research, face category, identity, and clustering, and gaze path and head pose estimation, in addition to purposes of face analysis.

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Additional info for Advances in Face Detection and Facial Image Analysis

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2. 65) C SN(1)*, the filtering steps are performed on the results of this detector. The performance after each filter or combination of filters is reported in Tables 4 and 5. The computation time reported in seconds is evaluated on a Xeon E5-1620 v2 – 30 L. Nanni et al. 7 GHz – 64 GB Ram using Matlab R2014a on a candidate region of 78 78 pixels without parallelizing the code (note, however, that the different filters can be run in parallel). It is clear that SIZE is the most useful criterion for removing the false positive candidates found by the ensemble of face detectors.

The four datasets have been merged to form a single larger dataset consisting of 549 images containing 614 faces (only upright frontal faces with a maximum rotation of ˙30ı have been considered). The parameter optimization of the face detectors has been performed manually and, despite the different origin and characteristics of the images included in the final set, the selected parameter optimizations have been fixed for all the images. The MERGED dataset is not an easy dataset to classify, as illustrated in Fig.

The performance after each filter or combination of filters is reported in Tables 4 and 5. The computation time reported in seconds is evaluated on a Xeon E5-1620 v2 – 30 L. Nanni et al. 7 GHz – 64 GB Ram using Matlab R2014a on a candidate region of 78 78 pixels without parallelizing the code (note, however, that the different filters can be run in parallel). It is clear that SIZE is the most useful criterion for removing the false positive candidates found by the ensemble of face detectors. The second best approach is the eye detector EYE.

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