Improving face gender classification by adding deliberately misaligned faces to the training data

A novel method of face gender classifier construction is proposed and evaluated. Previously, researchers have assumed that a computationally expensive face alignment step (in which the face image is transformed so that facial landmarks such as the eyes, nose, chin, etc, are in uniform locations in the image) is required in order to maximize the accuracy of predictions on new face images. We, however, argue that this step is not necessary, and that machine learning classifiers can be made robust to face misalignments by automatically expanding the training data with examples of faces that have been deliberately misaligned (for example, translated or rotated). To test our hypothesis, we evaluate this automatic training dataset expansion method with two types of image classifier, the first based on weak features such as Local Binary Pattern histograms, and the second based on SIFT keypoints. Using a benchmark face gender classification dataset recently proposed in the literature, we obtain a state-of-the-art accuracy of 92.5%, thus validating our approach

SIFTing the relevant from the irrelevant: Automatically detecting objects in training images

Many state-of-the-art object recognition systems rely on identifying the location of objects in images, in order to better learn its visual attributes. In this paper, we propose four simple yet powerful hybrid ROI detection methods (combining both local and global features), based on frequently occurring keypoints. We show that our methods demonstrate competitive performance in two different types of datasets, the Caltech101 dataset and the GRAZ-02 dataset, where the pairs of keypoint bounding box method achieved the best accuracies overall

Enhanced spatial pyramid matching using log-polar-based image subdivision and representation

This paper presents a new model for capturing spatial information for object categorization with bag-of-words (BOW). BOW models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching (SPM) technique is the most notable. We propose a new method to exploit spatial relationships between image features, based on binned log-polar grids. Our model works by partitioning the image into grids of different scales and orientations and computing histogram of local features within each grid. Experimental results show that our approach improves the results on three diverse datasets over the SPM technique

Improving Bag-of-Words model with spatial information

Bag-of-Words (BOW) models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching technique is the most notable. In this work, we propose three novel techniques to capture more re_ned spatial information between image features than that provided by the Spatial Pyramids. Our techniques demonstrate a performance gain over the Spatial Pyramid representation of the BOW model

3D face recognition using multiview keypoint matching

A novel algorithm for 3D face recognition based point cloud rotations, multiple projections, and voted keypoint matching is proposed and evaluated. The basic idea is to rotate each 3D point cloud representing an individual’s face around the x, y or z axes, iteratively projecting the 3D points onto multiple 2.5D images at each step of the rotation. Labelled keypoints are then extracted from the resulting collection of 2.5D images, and this much smaller set of keypoints replaces the original face scan and its projections in the face database. Unknown test faces are recognised firstly by performing the same multiview keypoint extraction technique, and secondly, the application of a new weighted keypoint matching algorithm. In an extensive evaluation using the GavabDB 3D face recognition dataset (61 subjects, 9 scans per subject), our method achieves up to 95% recognition accuracy for faces with neutral expressions only, and over 90% accuracy for face recognition where expressions (such as a smile or a strong laugh) and random faceoccluding gestures are permitted

Development of a music organizer for children

Software development for children is challenging; children have their own needs, which often are not met by ‘grown up’ software. We focus on software for playing songs and managing a music collection—tasks that children take great interest in, but for which they have few or inappropriate tools. We address this situation with the design of a new music management system, created with children as design partners: the Kids Music Box

Associated Higgs plus vector boson test of a fermiophobic Higgs boson

Production in association with an electroweak vector boson V is a distinctive mode of production for a Higgs boson H without tree-level couplings to fermions, known as a fermiophobic Higgs boson. We focus on HV associated production with H decay into a pair of photons, and V into a pair of jets, with the goal of distinguishing a fermiophobic Higgs boson from the standard model Higgs boson. Performing a simulation of the signal and pertinent QCD backgrounds, and using the same event selection cuts employed by the LHC ATLAS Collaboration, we argue that existing LHC data at 7 TeV with 4.9 fb^{-1} of integrated luminosity may distinguish a fermiophobic Higgs boson from a standard model Higgs boson near 125 GeV at about 1.9 standard deviation signal significance (1.9 sigma) per experiment. At 8 TeV we show that associated production could yield 2.8 sigma significance per experiment with 10 fb^{-1} of data.Comment: 5 pgs., 4 figs, version to appear in Phys. Rev.

Higgs-flavon mixing and LHC phenomenology in a simplified model of broken flavor symmetry

The LHC phenomenology of a low-scale gauged flavor symmetry model with inverted hierarchy is studied, through introduction of a simplified model of broken flavor symmetry. A new scalar (a flavon) and a new neutral top-philic massive gauge boson emerge with mass in the TeV range along with a new heavy fermion associated with the standard model top quark. After checking constraints from electroweak precision observables, we investigate the influence of the model on Higgs boson physics, notably on its production cross section and decay branching fractions. Limits on the flavon $\varphi$ from heavy Higgs boson searches at the LHC at 7 and 8 TeV are presented. The branching fractions of the flavon are computed as a function of the flavon mass and the Higgs-flavon mixing angle. We also explore possible discovery of the flavon at 14 TeV, particularly via the $\varphi \rightarrow Z^0Z^0$ decay channel in the $2\ell2\ell'$ final state, and through standard model Higgs boson pair production $\varphi \rightarrow hh$ in the $b\bar{b}\gamma\gamma$ final state. We conclude that the flavon mass range up to $500$ GeV could probed down to quite small values of the Higgs-flavon mixing angle with 100 fb$^{-1}$ of integrated luminosity at 14 TeV.Comment: 17 pages, 14 figure