Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of
  Isolated Facial Features and Foggy Faces   Gender classification aims at recognizing a person's gender. Despite the high
accuracy achieved by state-of-the-art methods for this task, there is still
room for improvement in generalized and unrestricted datasets. In this paper,
we advocate a new strategy inspired by the behavior of humans in gender
recognition. Instead of dealing with the face image as a sole feature, we rely
on the combination of isolated facial features and a holistic feature which we
call the foggy face. Then, we use these features to train deep convolutional
neural networks followed by an AdaBoost-based score fusion to infer the final
gender class. We evaluate our method on four challenging datasets to
demonstrate its efficacy in achieving better or on-par accuracy with
state-of-the-art methods. In addition, we present a new face dataset that
intensifies the challenges of occluded faces and illumination changes, which we
believe to be a much-needed resource for gender classification research.

10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int