The Perforated Curtain:: Configuring the Public and the Private in Calcutta’s Cabin Culture

When I was very young, eating out was not a regular activity for our family. On one of such rare occasion we ended up in a restaurant where the waiter made us sit in a cubicle with a curtain on one side

Deception/Truthful Prediction Based on Facial Feature and Machine Learning Analysis

The Automatic Deception detection refers to the investigative practices used to determine whether person is telling you Truth or lie. Automatic deception detection has been studied extensively as it can be useful in many real-life scenarios in health, justice, and security systems. Many psychological studies have been reported for deception detection.  Polygraph testing is a current trending technique to detect deception, but it requires human intervention and training.  In recent times, many machine learning based approaches have been applied to detect deceptions. Various modalities like Thermal Imaging, Brain Activity Mapping, Acoustic analysis, eye tracking. Facial Micro expression processing and linguistic analyses are used to detect deception. Machine learning techniques based on facial feature analysis look like a promising path for automatic deception detection. It also works without human intervention. So, it may give better results because it does not affect race or ethnicity. Moreover, one can do covert operation to find deceit using facial video recording. Covert Operation may capture the real personality of deceptive persons. By making combination of various facial features like Facial Emotion, Facial Micro Expressions and Eye blink rate, pupil size, Facial Action Units we can get better accuracy in Deception Detection

Determination of sex using cephalo-facial dimensions by discriminant function and logistic regression equations

Abstract:The aim is to bring together the new anthropological techniques and knowledge about populations that are least known. The present study was performed on 901 healthy Gujarati volunteers (676 males, 225 females) within the age group of 21–50years with the aim to examine whether any correlation exists between cephalofacial measures naming maximum head length, maximum head breadth, bizygomatic breadth, bigonial diameter, morphological facial length, physiognomic facial length, biocular breadth and total cephalofacial height and sex determination. Also, discriminant function and logistic regression methods were verified to check the best accuracy level for sex determination. Mean values of cephalofacial dimensions were higher in males than in females. Best reliable results were obtained by using logistic regression equations in males (92%) and discriminant function in females (80.9%). Our study conclusively establishes the existence of a definite statistically significant sexual dimorphism in Gujarati population using cephalo-facial dimensions