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Εφαρμογές αναγνώρισης προτύπων και μηχανικής μάθησης για ενσωματωμένα συστήματα

Kalodimas Panagiotis

Πλήρης Εγγραφή

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Περίληψη

In this thesis a new implementation of the “Face Detection, Pose Estimation, and Landmark Localization in the Wild” algorithm by Xiangxin Zhu and Deva Ramanan is represented. This implementation was firstly designed for being used by embedded systems but finally it can also be used by large multiprocessors systems. This is because the modern embedded systems tend to be similar to what we used to call multiprocessor systems years ago. Because of the huge needs of the market in the area of embedded systems (smart-phone, tablets and more) the latest embedded system are in the category of small multiprocessor systems using from 2 to 4 and even more cores in their central processing unit.Our implementation of the “Face Detection, Pose Estimation, and Landmark Localization in the Wild” algorithm was implemented in basic C\C++ as there is no usage of any external C\C++ library in the core of the algorithm. This gives the algorithm the ability to be used in both Windows and UNIX systems with no further changes. It also allows further improvements and alteration as it is easily readable for those who would like to use it for custom application. Our implementation gives the ability of customizing the functionality of the algorithm through a set of settings and parameters that can easily be modified.As this implementation is designed for usage in embedded systems the need of reducing memory consumption and processing speedup was encounter. For that reason a number of customizations were made in contrast to the original implementation of its creators. There were also produced a set of techniques that some may pull down the algorithm’s performance but in contrast they offer extra speedup and memory saving. These techniques may be very useful for custom application.Despite any further speedup the main problem of making the face detection task a great time consumer is the fact that the image size in the one that makes it a long time processing. Large images compel the system to create large image pyramids in order to search them for face detection. In addition the larger the top image is the more time is needed to be processed. The main solution on this problem is proposed is the scaling of the original image to a smaller size in order to reduce the number of data needed to be processed. This solution makes the systems faster but they lose part of their performance as scaling an image to a smaller size makes small size faces to be unable for detection. Our implementation offers a method that scans the image pyramid faster for face detections in order to avoid detection processing in pyramid levels that seems to be empty of faces. This can be a very effective method for video application where empty faces frames can be faster processed and rejected.

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