Just Like Faces, Buildings Have Features That Algorithms Can Recognize An art historian explains how he uses ‘facial recognition’ to unlock architectural secrets
Rather than viewing this as a failure, I realized I had found a new insight: Just as people’s faces have features that can be recognized by algorithms, so do buildings. That began my effort to perform facial recognition on buildings – or, more formally, “ architectural biometrics .” Buildings, like people, may just have biometric identities too.
Face detection is a great feature for cameras. When the camera can automatically pick out faces, it can make sure that all the faces are in focus before it takes the picture.
Thus image recognition works by looking for salient features, you can make algorithms that learns these features or you can handcraft them. So let’s look at how you can design handcrafted features for recognition. Here you are interested in an instance-level recognition system I assume.
What concepts and algorithms should I use to detect and recognize a person’s identity with NO face detection? What is the easiest way to write an app that shows facial landmarks on a webcam? I have written OpenCV face detection, but I am stuck.
Even as a face detector, if we manipulate the face a bit (say, cover up the eyes with sunglasses, or tilt the head to a side), a Haar-based classifier may not be able to recognize the face.
Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject’s face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. 
349 programs for «face recognition software» A face recognition algorithm is then employed to synthesize the H. Detect one or more human faces in an image and get back face rectangles for where in the image the faces are, along with face attributes which contain machine learning-based predictions of facial features.
Alan Slater, a psychologist at Exeter, concluded that babies enter the world with a highly detailed depiction of the human face which helps them recognize familiar faces. Babies used in the study ranged from a few hours old to two days old.
AdaBoost is a training process for face detection, which selects only those features known to improve the classification (face/non-face) accuracy of our classifier. In the end, the algorithm considers the fact that generally: most of the region in an image is a non-face region.
Practically said: if you learn the Fisherfaces for well-illuminated pictures only and you try to recognize faces in bad-illuminated scenes, then method is likely to find the wrong components (just because those features may not be predominant on bad illuminated images).
Maybe after processing a lot more data, something can happen. Recently, Google’s image recognition algorithm was able to perform with 93% efficiency. All the credits go to the massive data they have collected in the past. Just like that, the more faces Snapchat can process, higher will be the efficiency in applying the lenses.
Just like people, buildings have a life cycle, from the time they’re “born” (built) until they “die,” which can come in a number of ways, including fire, demolition or simply deterioration from the elements.
Zothecula writes «Scientists at Brigham Young University (BYU) have developed an algorithm that can accurately identify objects in images or videos and can learn to recognize new objects on its own. Although other object recognition systems exist, the Evolution-Constructed Features algorithm is nota