AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

Zhang

IEEE Trans. on Image Process.

et al. 2019

Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e.g., clustering) of compressed data. We therefore study in this paper a novel problem called compressive robust subspace clustering, which is to perform robust subspace clustering with the compressed data, and which is generated by projecting the original high-dimensional data onto a lowerdimensional subspace chosen at random. Given only the compressed data and sensing matrix, the proposed method, row space pursuit (RSP), recovers the authentic row space that gives correct clustering results under certain conditions. Extensive experiments show that RSP is distinctly better than the competing methods, in terms of both clustering accuracy and computational efficiency.

Section: Experiments and Resultsmentioning

confidence: 99%

Robust Subspace Clustering With Compressed Data

Zhang

IEEE Trans. on Image Process.

et al. 2019

“…We set the number of binaries 32 during experiments. Fixing all the values, we create a feature vector for color as F 16 COLOR ∈ R 48 .…”

Section: Output: Predicted Gendermentioning

confidence: 99%

Automatic Gender Classification through Face Segmentation

et al. 2019

Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes—mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all six classes. We use the probability maps as gender descriptors and trained a Random Decision Forest (RDF) classifier, which classifies the face images as either male or female. The performance of the proposed framework is assessed on four publicly available datasets, namely Adience, LFW, FERET, and FEI, with results outperforming state-of-the-art (SOA).

“…The SoF dataset [34] contains frontal and non-frontal for 112 persons (66 males and 46 females) with different facial expressions under harsh illumination environments. The dataset comprises 2,662 (640× 480 pixels) face images.…”

Section: The Specs On Face Datasetmentioning

confidence: 99%

Can we boost the power of the Viola–Jones face detector using preprocessing? An empirical study

et al. 2018

Self Cite

The Viola-Jones face detection algorithm was (and still is) a quite popular face detector. In spite of the numerous face detection techniques that have been recently presented, there are many research works that are still based on the Viola-Jones algorithm because of its simplicity. In this paper, we study the influence of a set of blind pre-processing methods on the face detection rate using the Viola-Jones algorithm. We focus on two aspects of improvement, specifically badly illuminated faces and blurred faces. Many methods for lighting invariant and deblurring are used in order to improve the detection accuracy. We want to avoid using blind pre-processing methods that may obstruct the face detector. To that end, we perform two sets of experiments. The first set is performed to avoid any blind pre-processing method that may hurt the face detector. The second set is performed to study the effect of the selected pre-processing methods on images that suffer from hard conditions. We present two manners of applying the pre-processing method to the image prior