Image detection
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Image detection involves locating objects in an image, while image classification involves labeling objects by category. Image recognition builds on these by detecting objects, classifying them, and recognizing specific instances. It has applications in areas like face recognition on smartphones. Image recognition technologies like those used by Facebook for face recognition have reached 98% accuracy, comparable to humans using multiple tagged photos for training.
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PBL presentation p2.pptx
Any camera in the last 10 years, probably has face detection in action:
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.
But this concept uses it for a different purpose — finding the areas of the image that has to be passed to the next step of the procedure.
To find faces in an image, the image has to be in the black and white format as colour data
is not necessary.
Now we are to the meat of the problem — actually telling faces apart. The solution is to train a Deep Convolutional Neural Network. But instead of training the network to recognize pictures objects like we did last time, we are going to train it to generate 128 measurements for each face.
The training process works by looking at 3 face images at a time:
Load a training face image of a known person.
Load another picture of the same known person.
Load a picture of a totally different person.
Then the algorithm looks at the measurements it is currently generating for each of those three images. It then tweaks the neural network slightly so that it makes sure the measurements it generates for #1 and #2 are slightly closer while making sure the measurements for #2 and #3 are slightly further apart.
After repeating this step millions of times for millions of images of thousands of different people, the neural network learns to reliably generate 128 measurements for each person. Any ten different pictures of the same person should give roughly the same measurements.
Machine learning people call the 128 measurements of each face an embedding. The idea of reducing complicated raw data like a picture into a list of computer-generated numbers comes up a lot in machine learning.
This last step is actually the easiest step in the whole process. All we have to do is find the person in our database of known people who has the closest measurements to our test image that we show in front of the web cam.
After doing the comparative analysis by processing all the known images, our software finally tells the result of test image by displaying its name.
We did it by using any basic machine learning classification algorithm. No fancy deep learning tricks are needed.
When, we isolated the faces in our image. But now we have to deal with the problem that faces turned different directions look totally different to a computer:
To account for this, we will try to warp each picture so that the eyes and lips are always in the sample place in the image. To do this, we are going to use an algorithm called face landmark estimation .
The basic idea is we will come up with 68 specific points (called landmarks) that exist on every face — the top of the chin, the outside edge of each eye, the inner edge of each eyebrow, etc. Then we will train a machine learning algorithm to be able to find these 68 specific points on any face.
It has successfully run the alg
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Any camera in the last 10 years, probably has face detection in action:
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.
But this concept uses it for a different purpose — finding the areas of the image that has to be passed to the next step of the procedure.
To find faces in an image, the image has to be in the black and white format as colour data
is not necessary.
Now we are to the meat of the problem — actually telling faces apart. The solution is to train a Deep Convolutional Neural Network. But instead of training the network to recognize pictures objects like we did last time, we are going to train it to generate 128 measurements for each face.
The training process works by looking at 3 face images at a time:
Load a training face image of a known person.
Load another picture of the same known person.
Load a picture of a totally different person.
Then the algorithm looks at the measurements it is currently generating for each of those three images. It then tweaks the neural network slightly so that it makes sure the measurements it generates for #1 and #2 are slightly closer while making sure the measurements for #2 and #3 are slightly further apart.
After repeating this step millions of times for millions of images of thousands of different people, the neural network learns to reliably generate 128 measurements for each person. Any ten different pictures of the same person should give roughly the same measurements.
Machine learning people call the 128 measurements of each face an embedding. The idea of reducing complicated raw data like a picture into a list of computer-generated numbers comes up a lot in machine learning.
This last step is actually the easiest step in the whole process. All we have to do is find the person in our database of known people who has the closest measurements to our test image that we show in front of the web cam.
After doing the comparative analysis by processing all the known images, our software finally tells the result of test image by displaying its name.
We did it by using any basic machine learning classification algorithm. No fancy deep learning tricks are needed.
When, we isolated the faces in our image. But now we have to deal with the problem that faces turned different directions look totally different to a computer:
To account for this, we will try to warp each picture so that the eyes and lips are always in the sample place in the image. To do this, we are going to use an algorithm called face landmark estimation .
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Explain the difference between face detection and face recognition. Give an example use of each
in IT.
Solution
solution:
Face detection is a program that determines the locations of human faces in a digital image.
Face recognition is a program that identifies a person in a digital image.
There are several notions that should be distinguished.
Classification = partition of a set of observable objects into disjoint similarity classes (maybe,
constituting a hierarchical structure).
Detection = in a large set of objects finding out all those belonging to a certain similarity class.
Recognition = for a given object answering, to what of (a priori defined) similarity classes it
belongs. Identification = a particular case of recognition: proving that a given object really
belongs to a similarity class being declared.
Face detection aims at the detection/location of face in an image while Face recognition aims at
identifying the face with some known faces.
The best example for face detection is our Digital Cameras, you see a square over the faces,
when taking photos. Face recognition is usually used in forensics to identify fugitives from street
cameras... or something like that.
face detection is an attempt to detect faces in an image (or video, I guess). Face detection
software will typically provide the location and face size/orientation of each region of the image
it feels is probably a face. In my experience, the results can be quite noisy. Depending on how
the detection parameters are set, you may get many non-faces and will likely miss some/many
faces (especially if they are partially obscured).
Face recognition, on the other hand, attempts to identify a face in an image that is known (or
thought to) contain a single face. Face detection may be applied first to extract image segments
containing faces. A database of face data for individuals is needed, and the face recognition
software will attempt to associate the provided image with one or more records in the database,
typically with a probability that the faces match.
Eigenfaces & Fisherfaces
Those familiar with linear algebra will remember that every vector space has an orthogonal
basis. By combining elements of this basis we can compose every vector in this vector space.
And vice versa, every vector in the vector space can be decomposed to the elements of the basis.
Images (grayscale) are nothing more than a series of numbers, each number corresponding to
some intensity level. So why not treat images as vectors? Say, for example, we have a collection
of face images of size 150 by 150 pixels; each of these images can be thought of as a vector of
size 22,500 (150*150). We can now talk about the vector space in which these vectors reside. By
treating the images as samples of data, we can perform a Principal Components Analysis and
obtain the eigenvectors which make up the basis of the vector spa.
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This document describes a face emotion detection system called Emotionalizer. It uses machine learning and facial recognition techniques to detect emotions like happy, sad, angry, fearful and disgust based on facial expressions. The system analyzes images of faces and determines the appropriate emotion based on geometric changes in facial features. It was developed in Python using tools like OpenCV for facial detection and recognition. The goal is to build a system that can read emotions from facial expressions similarly to how humans perceive emotions.
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Detection versus recognition ... Investigate Face++ at Feel free to.pdf
Detection versus recognition ... Investigate Face++ at Feel free to browse to other similar sites
Explain the difference between face detection and face recognition. Give an example use of each
in IT.
Solution
solution:
Face detection is a program that determines the locations of human faces in a digital image.
Face recognition is a program that identifies a person in a digital image.
There are several notions that should be distinguished.
Classification = partition of a set of observable objects into disjoint similarity classes (maybe,
constituting a hierarchical structure).
Detection = in a large set of objects finding out all those belonging to a certain similarity class.
Recognition = for a given object answering, to what of (a priori defined) similarity classes it
belongs. Identification = a particular case of recognition: proving that a given object really
belongs to a similarity class being declared.
Face detection aims at the detection/location of face in an image while Face recognition aims at
identifying the face with some known faces.
The best example for face detection is our Digital Cameras, you see a square over the faces,
when taking photos. Face recognition is usually used in forensics to identify fugitives from street
cameras... or something like that.
face detection is an attempt to detect faces in an image (or video, I guess). Face detection
software will typically provide the location and face size/orientation of each region of the image
it feels is probably a face. In my experience, the results can be quite noisy. Depending on how
the detection parameters are set, you may get many non-faces and will likely miss some/many
faces (especially if they are partially obscured).
Face recognition, on the other hand, attempts to identify a face in an image that is known (or
thought to) contain a single face. Face detection may be applied first to extract image segments
containing faces. A database of face data for individuals is needed, and the face recognition
software will attempt to associate the provided image with one or more records in the database,
typically with a probability that the faces match.
Eigenfaces & Fisherfaces
Those familiar with linear algebra will remember that every vector space has an orthogonal
basis. By combining elements of this basis we can compose every vector in this vector space.
And vice versa, every vector in the vector space can be decomposed to the elements of the basis.
Images (grayscale) are nothing more than a series of numbers, each number corresponding to
some intensity level. So why not treat images as vectors? Say, for example, we have a collection
of face images of size 150 by 150 pixels; each of these images can be thought of as a vector of
size 22,500 (150*150). We can now talk about the vector space in which these vectors reside. By
treating the images as samples of data, we can perform a Principal Components Analysis and
obtain the eigenvectors which make up the basis of the vector spa.
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Image detection
- 1. History .in 1960, the first semi-automated system for facial recognition to locate the features (such as eyes,ears,nose and mouth)on the photographs. .In 1970s, Gold Stein and Harmon used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. .In 1988,kirby and sirvoich used standard linear algebra technique to the face recognition. . The four features matched by this algorithm are then sought in the image of a face (show at left). . Rectangle features. . value=(pixel in black area)_(pixel in white area). .Three types:two,three, four-rectangle,viola and jones used two rectangle features.
- 2. .For example: the difference in brightness between the white and black rectangle over a specific area. .Each features is related to a special location in the sub window. What is image detection? Image or object detection is a computer technology that process the image and detect object in it. People often confused image detection with image classification. Although the difference is rather clear. If you need to classify image items,you use classification,but if you just need to locate them,for example find out the no. Of objects in the picture, you should use image detection.
- 3. What is image classification? It is a process of labeling objects in the image sorting them by certain classes. For example,ask Google to find pictures of dogs and the network will fetch even drawing with dogs. It is a more advanced version of image detection. Now the neural network has to process different images with different objects, detect them and classify by the type of the item on the picture. What is image Recognition? Image Recognition is the ability of AI to detect the object., classify and recognize it. The last step is close to the human level of image processing. The best example of image Recognition solutions is the face recognition- say to unblock your smartphone you have to let
- 4. It scan your face. So first of all, the system has to detect the face,then classify it as a human face and only then decide if it belongs to the owner of the smart phone.As you can see it is a rather complicated process.
- 7. How image Recognition Technology Actually Work? Facebook can now perform face recognition at 98% accuracy which is comparable to the ability of humans. Facebook can identify your friends face with only a few tagged pictures. The efficiency of this technology depends on the ability to calssify images. Examples: . Forensics. . photography. . cameras . Medical imaging. . Machine vision (robotics) . Manufacturing testing. . scanners . video processing (tv, monitor, displays)
- 8. Advantages: .Relative simple .More sensitive then photo graphic film . cheaper to replace if failure . Detector costs simple .No chemical processing is needed .Astronomical imaging . Infarred detection and imaging . Signal processing . Digital photography . Medical fluoroscopy
- 9. Disadvantages: . Demagnification is a major issue . Vary with application . Very expensive . Relates to potentially lower DQE