Image Recognition: A peek into the future

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Our brains are wired in a way that they can differentiate between objects, both living and non-living by simply looking at them. In fact, the recognition of objects and a situation through visualization is the fastest way to gather, as well as to relate information. This becomes a pretty big deal for computers where a vast amount of data has to be stuffed into it, before the computer can perform an operation on its own. Ironically, with each passing day, it is becoming essential for machines to identify objects through facial recognition, so that humans can take the next big step towards a more scientifically advanced social mechanism. So, what progress have we really made in that respect?


Private Tokyo girls' school in deep ferment

The Japan Times

Trouble is brewing among the students of the Girls Domestic Science School, a well-known private institution at Hitotsubashi, Kanda, which enjoys a good reputation in educational circles and has contributed greatly to the advancement of female education, the courses including sewing, embroidery and foreign-style cooking. The school recently received a monetary donation amounting to ¥13,000 from Mr. Kamesaburo Yamashita, the well-known "narikin" of Kobe, who has amassed a big fortune though the sale of steamers. Several days ago the girls school referred to had a visit from an aged lady, who was alleged to have been sent by Mr. Yamashita, the patron of the school, on the mission of selecting a prospective bride for the son or nephew of the narikin. The old lady was treated by the school faculty with marked respect, and as though she came with the object of inspection, the true purpose of her visit being hidden as far as possible. Madame Haruko Hatoyama, the widow of the late Dr. Hatoyama, ex-minister of justice and dean of Waseda who is the superintendent of the teaching staff of the school, ordered the class to stop the lesson and gave the visitor the privilege of leisurely examining the personal beauty of the girl students of the graduating class of a certain course.


Automatic Age Detection by Neural Network in an Image

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The neural network facilitates automation image processing facilities which are either number plate recognition, face detection, expression recognition, joining images at different exposures etc. It also eases the task to recognize and verify the ages from the images which help in passports and other related applications. This MATLAB code for age detection from images contributes towards that fact. We developed this code at free-thesis. We used FGNET aging dataset for the features generation and testing.


New AI can work out whether you're gay or straight from a photograph

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Artificial intelligence can accurately guess whether people are gay or straight based on photos of their faces, according to new research suggesting that machines can have significantly better "gaydar" than humans. The machine intelligence tested in the research, which was published in the Journal of Personality and Social Psychology and first reported in the Economist, was based on a sample of more than 35,000 facial images that men and women publicly posted on a US dating website. The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and vice versa. The data also identified certain trends, including that gay men had narrower jaws, longer noses and larger foreheads than straight men, and that gay women had larger jaws and smaller foreheads compared to straight women.


New AI can tell whether you're gay or straight from a photograph

The Guardian

Artificial intelligence can accurately predict whether people are gay or straight based on photos of their faces, according to new research suggesting that machines can have significantly better "gaydar" than humans. The study from Stanford University – which found that a computer algorithm could correctly distinguish between gay and straight men 81% of the time, and 74% for women – has raised questions about the biological origins of sexual orientation, the ethics of facial-detection technology and the potential for this kind of software to violate people's privacy or be abused for anti-LGBT purposes. The machine intelligence tested in the research, which was published in the Journal of Personality and Social Psychology and first reported in the Economist, was based on a sample of more than 35,000 facial images that men and women publicly posted on a US dating website. The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and visa versa.