Detektor twarzy Viola-Jones#

Przykładowy projekt Viola-Jones dołączony do CMUcam3 jest przykładem lekkiego detektora twarzy. Algorytm opiera się na dobrze znanym artykule „Robust Real-Time Face Detection” autorstwa P. Violi i M. Jonesa z 2004 roku (viola-ijcv04.pdf). Implementacja na CMUcam3 zwróci współrzędne ramek, w których wykryje twarz w obrazie. Jeśli przetestujesz ten kod na stosunkowo jednorodnym tle (jak biała ściana), działa całkiem dobrze. Obrazy są trenowane z bazy danych twarzy CMU tak, aby uogólniały się na wszystkie twarze. Czasami niektóre twarze z cechami niewystępującymi w bazie danych mogą zmylić detektor.

Sample Face 1 Sample Face 2

The paper (viola-ijcv04.pdf) introduces a novel technique to detect faces in real-time and with very high detection rate. It is essentially a feature-based approach in which a classifier is trained for Haar-like rectangular features selected by Adaboost. The test image is scanned at different scales and positions using a rectangular window, and the regions which pass the classifier are declared as faces. One of the major contributions of this paper is the extremely rapid computation of these features using the concept of Integral Image, which enables the detection in real-time. Additionally, instead of learning a single classifier and computing all the features for all the scanning windows in the image, a number of classifiers are learnt which are put together in a series to form a cascade. The classifiers in the beginning of the cascade are simpler and consist of smaller numbers of features. However, as one proceeds in the cascade, the classifiers become more complex. A region is reported as detection only if it passes all the classifier stages in the cascade. If it is rejected at any stage, it is discarded and not processed further. This way, the easier patches in the image which the „cascade of classifiers” is sure of not being a face, are rejected very early while the difficult regions are operated on by more complex classifiers. This greatly speeds up the detection process without compromising on the accuracy and provides high detection rate. This overall system provides performance comparable to the existing best face detector systems (Rowley et al., 1998 CVPR00.pdf; Schneiderman and Kanade, 2000 nips00.pdf; Roth at al., 2000 rowley-ieee.pdf) but with orders of magnitudes faster than any of these systems. On a conventional desktop, it can detect faces at 15 frames per second.

Więcej informacji o detektorze twarzy Viola-Jones i jego implementacji na CMUcam3 można znaleźć w naszym dokumencie CC3 Face Detector.

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