From Event: SPIE Optical Engineering + Applications, 2016
In video surveillance semantic traits estimation as gender and age has always been debated topic because of the
uncontrolled environment: while light or pose variations have been largely studied, defocused images are still rarely
investigated. Recently the emergence of new technologies, as plenoptic cameras, yields to deal with these problems
analyzing multi-focus images. Thanks to a microlens array arranged between the sensor and the main lens, light field
cameras are able to record not only the RGB values but also the information related to the direction of light rays: the
additional data make possible rendering the image with different focal plane after the acquisition. For our experiments,
we use the GUC Light Field Face Database that includes pictures from the First Generation Lytro camera. Taking
advantage of light field images, we explore the influence of defocusing on gender recognition and age estimation
Evaluations are computed on up-to-date and competitive technologies based on deep learning algorithms. After studying
the relationship between focus and gender recognition and focus and age estimation, we compare the results obtained by
images defocused by Lytro software with images blurred by more standard filters in order to explore the difference
between defocusing and blurring effects. In addition we investigate the impact of deblurring on defocused images with
the goal to better understand the different impacts of defocusing and standard blurring on gender and age estimation.
V. Chiesa and J. L. Dugelay, "Impact of multi-focused images on recognition of soft biometric traits," Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99710Q (Presented at SPIE Optical Engineering + Applications: August 30, 2016; Published: 27 September 2016); https://doi.org/10.1117/12.2237297.
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