The Computer Nose Best The nose is the most central feature on the face which is known to exhibit both gender and ethnic differences. It is a robust feature, invariant to expression and known to contain depth information. In this paper we address the topic of binary ethnicity classificiation from images of the nose, using a novel dataset of South Asian, Pakistani images. To the best of our knowledge, we are one of the first to attempt demographic (ethnicity) based identification based solely on information from the nose.A twocategory (Pakistani vs NonPakistani) task was used in combination with Deep learning (ResNet) based and VGGbased pretrained models. A series of experiments were conducted using ResNet50, ResNet101, ResNet152, VGGFace, VGG16 and VGG19, for feature extraction and a Linear Support Vector Machine for classification. The experimental results demonstrate ResNet50 achieves the highest performance accuracy of 94.1%. In comparison, the highest score for the VGGbased models (VGG16) was 90.8%. These results demonstrate that information from the nose is sufficient for deep learning models to achieve u003e90% accuracy on judgements of ethnicity.

Proximity Search Method for Mining Biomedical and Genomic Information The human face is an important part of the human body. Finding SNPs associated with its different regions can open doors to deeper understanding of different genetic diseases affecting the face, improve genotypetophenotype analysis for forensics, etc. In this work we propose a process for constructing a panel of SNPs associated with the human face leveraging a database of published work on different regions of the face and applying proximity search to ensure relevant selections of publications. We validate the process by selecting top SNPs ranked using standard metrics to make predictions on human biogeographical ancestry at the continent level. Although our process makes no assumptions on ancestry informativeness of SNPs, our panel, grouped by chromosomes, performs comparatively well at the task of ancestry classification. Our results highlight geneschromosomes that promise better discriminative power to help those under resource constraint focus on only a few geneschromosomes for further analyses.