Human face profile recognition

The purpose of this thesis is to implement an automatic person identification system based on face profiles. Each person\u27s face profile can be quite unique within a small sample population and therefore it can be used as the basis of an automatic person identification system. To quantify human face profiles for use in the recognition system, Fourier descriptors are used to describe the open curve extracted from a face profile. Fourier descriptors in the low-frequency range are shown to be useful for human face profile recognition. By using 16 Fourier coefficients, a correct recognition rate of 92% for 60 subjects was achieved

Automatic features characterization from 3d facial images.

This paper presents a novel and computationally fast method for automatic identification of symmetry profile from 3D facial images. The algorithm is based on the concepts of computational geometry which yield fast and accurate results. In order to detect the symmetry profile of a human face, the tip of the nose is identified first. Assuming that the symmetry plane passes through the tip of the nose, the symmetry profile is then extracted. This is undertaken by means of computing the intersection between the symmetry plane and the facial mesh, resulting in a planner curve that accurately represents the symmetry profile. Experimentation using two different 3D face databases was carried out, resulting in fast and accurate results

Building a Corpus of 2L English for Automatic Assessment: the CLEC Corpus

In this paper we describe the CLEC corpus, an ongoing project set up at the University of Cádiz with the purpose of building up a large corpus of English as a 2L classified according to CEFR proficiency levels and formed to train statistical models for automatic proficiency assessment. The goal of this corpus is twofold: on the one hand it will be used as a data resource for the development of automatic text classification systems and, on the other, it has been used as a means of teaching innovation techniques

Automatic detection of spermatozoa for laser capture microdissection

In sexual assault crimes, differential extraction of spermatozoa from vaginal swab smears is often ineffective, especially when only a few spermatozoa are present in an overwhelming amount of epithelial cells. Laser capture microdissection (LCM) enables the precise separation of spermatozoa and epithelial cells. However, standard sperm-staining techniques are non-specific and rely on sperm morphology for identification. Moreover, manual screening of the microscope slides is time-consuming and labor-intensive. Here, we describe an automated screening method to detect spermatozoa stained with Sperm HY-LITER (TM). Different ratios of spermatozoa and epithelial cells were used to assess the automatic detection method. In addition, real postcoital samples were also screened. Detected spermatozoa were isolated using LCM and DNA analysis was performed. Robust DNA profiles without allelic dropout could be obtained from as little as 30 spermatozoa recovered from postcoital samples, showing that the staining had no significant influence on DNA recovery

VGGFace2: A dataset for recognising faces across pose and age

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS- Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on all the IARPA Janus face recognition benchmarks, e.g. IJB-A, IJB-B and IJB-C, exceeding the previous state-of-the-art by a large margin. Datasets and models are publicly available.Comment: This paper has been accepted by IEEE Conference on Automatic Face and Gesture Recognition (F&G), 2018. (Oral