"This paper proposes a novel adaptive algorithm to extract facial feature points automatically such as eyebrows corners, eyes corners, nostrils, nose tip, and mouth corners in frontal view faces, which is based on cumulative histogram approach by varying different threshold values. At first, the method adopts the Viola-Jones face detector to detect the location of face and also crops the face region in an image. From the concept of the human face structure, the six relevant regions such as right eyebrow, left eyebrow, right eye, left eye, nose, and mouth areas are cropped in a face image. Then the histogram of each cropped relevant region is computed and its cumulative histogram value is employed by varying different threshold values to create a new filtering image in an adaptive way. The connected component of interested area for each relevant filtering image is indicated our respective feature region. A simple linear search algorithm for eyebrows, eyes and mouth filtering images and contour algorithm for nose filtering image are applied to extract our desired corner points automatically. The method was tested on a large BioID frontal face database in different illuminations, expressions and lighting conditions and the experimental results have achieved average success rates of 95.27%."

A fundamental assumption in neuroscience is that brain structure determines function. Accordingly, functionally distinct regions of cortex should be structurally distinct in their connections to other areas. We tested this hypothesis in relation to face selectivity in the fusiform gyrus. By using only structural connectivity, as measured through diffusion-weighted imaging, we were able to predict functional activation to faces in the fusiform gyrus. These predictions outperformed two control models and a standard group-average benchmark. The structure-function relationship discovered from the initial participants was highly robust in predicting activation in a second group of participants, despite differences in acquisition parameters and stimuli. This approach can thus reliably estimate activation in participants who cannot perform functional imaging tasks and is an alternative to group-activation maps. Additionally, we identified cortical regions whose connectivity was highly influen

"You should be able to delete the information that we know about you, at least that we control," he said.

Schmidt said that while "in general we [Google] take the position that you own your data and should be able to opt in or out of a service, the search engine will be able to improve its offerings if users agree to share data with Google.

"If you choose to give us that information we can do a better job. If we know a little bit more about you we can offer better targeted search."

Max is a 24 year old law student from Vienna with a flair for the interview and plenty of smarts about both technology and legal issues. In Europe there is a requirement that entities with data about individuals make it available to them if they request it. That’s how Max ended up with a personalized CD from Facebook that he printed out on a stack of paper more than a thousand pages thick (see image below). Analysing it, he came to the conclusion that Facebook is engineered to break many of the requirements of European data protection. He argues that the record Facebook provided him finds them to be in flagrante delicto.

Shadow Profiles.
Facebook is collecting data about people without their knowledge. This information is used to substitute existing profiles and to create profiles of non-users.

Synchronizing.
Facebook is gathering personal data e.g. via its iPhone-App or the “friend finder”. This data is used by Facebook without the consent of the data subjects.

Postings on other Users’ Pages.
Users cannot see the settings under which content is distributed that they post on other’s pages.

Messages.
Messages (incl. Chat-Messages) are stored by Facebook even after the user “deleted” them. This means that all direct communication on Facebook can never be deleted.

Privacy Policy and Consent.
The privacy policy is vague, unclear and contradictory. If European and Irish standards are applied, the consent to the privacy policy is not valid.

Face Recognition.
The new face recognition feature is an inproportionate violation of the users right to privacy. Proper information and an unambiguous consent of the users is missing.

Access Request.
Access Requests have not been answered fully. Many categories of information are missing.

Data Security.
In its terms, Facebook says that it does not guarantee any level of data security.

Excessive processing of Data.
Facebook is hosting enormous amounts of personal data and it is processing all data for its own purposes.
It seems Facebook is a prime example of illegal “excessive processing”.

Like Button.
The Like Button is creating extended user data that can be used to track users all over the internet. There is no legitimate purpose for the creation of the data. Users have not consented to the use.

New Policies.
The policies are changed very frequently, users do not get properly informed, they are not asked to consent to new policies.

Back when the iPad was still just a rumor, the Wall Street Journal reported that one of the features that Apple had been working on with their upcoming tablet was the ability to recognize users by face.
One person familiar with the matter said Apple has put significant resources into designing and programming the device so that it is intuitive to share. This person said Apple has experimented with the ability to leave virtual sticky notes on the device and for the gadget to automatically recognize individuals via a built-in camera. It is unclear whether these features will be included at launch.

"Recently, the face recognizers based on linear representations have been shown to deliver state-of-the-art performance. In real-world applications, however, face images usually suffer from expressions, disguises and random occlusions. The problematic facial parts undermine the validity of the linear-subspace assumption and thus the recognition performance deteriorates significantly. In this work, we address the problem in a learning-inference-mixed fashion. By observing that the linear-subspace assumption is more reliable on certain face patches rather than on the holistic face, some Bayesian Patch Representations (BPRs) are randomly generated and interpreted according to the Bayes' theory. We then train an ensemble model over the patch-representations by minimizing the empirical risk w.r.t the "leave-one-out margins". The obtained model is termed Optimal Representation Ensemble (ORE), since it guarantees the optimality from the perspective of Empirical Risk Minimization. To handle the unknown patterns in test faces, a robust version of BPR is proposed by taking the non-face category into consideration. Equipped with the Robust-BPRs, the inference ability of ORE is increased dramatically and several record-breaking accuracies (99.9% on Yale-B and 99.5% on AR) and desirable efficiencies (below 20 ms per face in Matlab) are achieved. It also overwhelms other modular heuristics on the faces with random occlusions, extreme expressions and disguises. Furthermore, to accommodate immense BPRs sets, a boosting-like algorithm is also derived. The boosted model, a.k.a Boosted-ORE, obtains similar performance to its prototype. Besides the empirical superiorities, two desirable features of the proposed methods, namely, the training-determined model-selection and the data-weight-free boosting procedure, are also theoretically verified."