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         author = {Subburaman, Venkatesh Bala},
       keywords = {Binary features, decision tree, Face Detection, interest point., location estimation},
       projects = {Idiap, IM2, MOBIO},
          month = mar,
          title = {Alternative search techniques for face detection using location estimation and binary features},
           year = {2012},
       abstract = {The sliding window approach is the most widely used technique to detect objects
from an image. In the past few years, classifiers have been improved in many ways to
increase the scanning speed. Apart from the classifier design (such as the cascade), the
scanning speed also depends on a number of different factors (such as grid spacing, and
scale at which the image is searched). Scanning grid spacing controls the number of
subwindows being processed, thus controlling the speed of detection.When the scanning
grid spacing is larger than the tolerance of the trained classifier it can suffer from low
detections. In this thesis, we propose an alternative search technique, which can improve
the detections when lesser number of subwindows are processed.

First, we present a technique to reduce the number of miss detections while increasing
the grid spacing when using the sliding window approach for object detection. This
is achieved by using a small patch to predict the location of an object within a local
search area. To achieve speed, it is necessary that the time taken for location prediction
is comparable or better than the time it takes in average for the object classifier to reject
a subwindow. We use binary features and a decision tree as it proved to be efficient
for our application. In the process we also propose a variation of an existing binary feature
(Ferns) with similar performance, and requires only half the number of pixel access
when compared to Fern feature. We analyze the effect of patch size on location estimation
and also evaluate our approach on several face databases. Experimental evaluation
shows better detection rate and speed with our proposed approach for larger grid spacing
(lesser number of subwindows) when compared to standard scanning technique.

We also show that by using a simple interest point detector based on quantized gradient
orientation, as the front-end to the proposed location estimation technique, we can
achieve better performance even when fewer number of subwindows are processed. The
interest points detected can be assumed as a non-regular grid compared to regular grid
in the sliding window framework. A few image patches are sampled around an interest
point for estimating the probable face location and further verified using a strong face
classifier. Experiment results show that using an interest point detector can reduce the
number of subwindows processed while maintaining a good detection rate.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Subburaman_THESIS_2012.pdf}