Computer Vision Solutions | Advanced Image Processing

Face Recognition: A New Way Of Security Many biometric techniques are used for identifying humans such as signature, fingerprint, speech, face, and hand geometric recognition. Of these, face recognition techniques is the simplest and most consistent. It is because facial recognition does not require active human cooperation. The basic functionality of face recognition goes through - verification, photography, identification, and result. While finding and knowing a comprehensive photo database is a daunting task, this biometric software application works as a reliable and robust security system. It is used in various fields such as driver's license system, ATMs, passport verification, rail booking, mobile platforms, and other monitoring and evaluation functions. Source: Shutter Stock WHAT IS FACE RECOGNITION? Face recognition is a technology that can identify or verify a topic with an image, video, or any visual object with its face. Typically, this identifier is used to access a program, program, or service. It is a biometric identification method that uses those physical steps, in this case, the face and head, to verify a person's identity with a pattern of biometric data and data. The technology collects a unique set of individual biometric data associated with face-to-face identification, verification, and/or authentication. In-depth Learning Programs Used for Face Recognition Currently, these are four well-known DL programs that work together DeepFace DeepID series of programs VGGFace FaceNet DeepFace- According to Deep convolutional neural networks, DeepFace is an in-depth face recognition program. Created by Facebook, it detects and determines the identity of a person's face through digital photography, which is reported to be 97.35% accurate. DeepID- It was first invented by Yi Sun in his paper Deep Learning Face Representation from predicting 10,000 classes, secret identity of the discovery of a common object, which is counted among the first models of in-depth face-to-face learning. DeepID has gained more accuracy than people in the project. VGGFace- By Omkar Parkhi, Andrea Vedaldi, and Andrew Zisserman of VGG (Visual Geometry Group) in Oxford for their paper “Deep Face Recognition.” This paper has contributed to understanding the construction of the enormous data needed to train CNN's modern face recognition systems. A set of available data is then used as the basis for CNN's deep development of visual functions. FaceNet- To achieve technical results in standard data sets, FaceNet uses a three-loss function to study vectors for better results in feature extraction and, consequently, authentication. FACE RECOGNITION SYSTEM Face Recognition / Biometric Face Technology has a wide variety of applications; for example, using the built-in camera, tablet, or computer, face recognition software can change the passwords of the device account and users' access passwords. In law enforcement, technology can assist in the identification of a suspect, while border controls can be used to make security operations more consistent. Another popular system for face recognition programs is to control access to a high-value area. In the commercial sector, retailers and retailers use technology as a means of collecting important personal information. The facial procedure can make two variations depending on when it is performed: That is, for the first time, a face-to-face recognition system to register and associate you with identity, in the sense that it is recorded in the system. This process is also known as digital onboarding with face recognition. The exception is where the user is verified, before registration. In this process, incoming data from the camera falls through the existing data in the database. If the face matches the registered ID, the user is given access to the system with his or her credentials. HOW DOES THIS WORKS? Face recognition systems capture incoming images from a camera device in a three- or three-dimensional way depending on the device's features. These compare the relevant details of the incoming image signal in real-time on a photo or video in a database, which is more reliable and secure than the information obtained from the still image. This biometric face recognition process requires an internet connection because the database cannot be accessed on the capture device as it is hosted on servers. In this face comparison, we statistically analyze the incoming image without the error limit and confirm that the biometric data is the same as the person who should use the service or request access to the program, program, or structure. Thanks to the use of artificial intelligence (AI) and machine learning technology, face recognition systems can operate with the highest standards of safety and reliability. Similarly, due to the combination of these algorithms and computer techniques, the process can be performed in real-time. BIOMETRIC FACIAL RECOGNITION Face recognition uses a focus on validation or validation. These technologies are used, for example, in situations such as: The second authentication feature is to add additional security to any login process. Access to mobile applications without a password. Access to pre-contracted Internet services (sign in to online platforms, for example). Access to Hotels, Building, Offices, etc. How to pay, both in physical stores and online. Access to a locked device. Access guest services (airports, hotels…). SMILEID, THE SOLUTION TO THE STANDARD BIOMETRIC FACIAL RUNNION At Electronic Identification SmileID is developed, faomet-based bio-metric recognition solution

Area Of Work: Computer Vision

Vikas Verma

29 Sep 2020

Understanding the CNN for Computer Vision Applications Convolutional Neural Networks or CNN is a type of deep neural networks that are efficient at extracting meaningful information from visual imagery. As an experiential AI Development Company, Oodles AI decodes the underlying layers of CNN and how businesses can deploy CNN for computer vision applications. When it comes to us, humans, evolution has gifted us with very complex yet efficient techniques to view and detect several objects. Our brain keeps on learning continuously without our notice. There are several organs and parts of our brain involved in the process like eyes, receptors and visual cortex. In the era, with the resources and immense computational power, it would be pointless not to explore computer vision. With so many applications of computer vision services, we can take current generation technology to the next level. A great example is the upcoming Tesla's Robo-taxi which gives us a glimpse into the future. A very popular machine learning algorithm, especially for Object Detection, is Convolutional Neural Networks or CNN. CNN consists of four hidden layers such as- Convolutional layers Pooling layers fully connected layers, and Normalization layers. Convolutional Layers takes two input layers - a part of the image and an equally sized filter called the kernal. The output of this layer is the dot product of both inputs. The idea of Pooling is to down-sample data. The Pooling Layer takes the input (an image) and reduces its size in terms of a number of pixels. There are two ways to perform this - Max Pooling and Min Pooling. Max Pooling picks the maximum value from the selected region, whereas Min Pooling picks up the minimum value. Under Fully Connected Layers, as the name suggests, all the outputs from one layer are connected to the input of another layer. These layers are useful in the classification of the data. Normalization Layers are used to stabilize the neural networks. It performs normalization on the input data. CNN performs incredibly when it comes to analyzing a single image, but it lacks one essential quality - they only consider spatial features and visual data ignoring the temporal and time features i.e., how a frame is related to the previous frame. This is where Recurrent Neural Networks or RNN come into play. The term ‘recurrent' suggests that the neural network repeats the same tasks for every sequence. RNN can also be used in Natural Language Processing. Employing CNN for Computer Vision Applications with Oodles AI Oodles AI is a team of seasoned professionals working with artificial intelligence technologies including machine learning and deep learning to build next-gen solutions. We have hands-on expertise in deploying CNN and RNN models for applications such as the image caption generating model. In addition, our AI capabilities encompass- Predictive Analytics Machine learning Recommendation systems Natural Language Processing, and Chatbot Development Reach out to our AI team to know more about our artificial intelligence services.

Area Of Work: Computer Vision Industry: Software Development

Asheesh Bhuria

28 Jan 2020

Learn Image Text Recognition Using Google Cloud Vision API According to Google Cloud Vision API Documentation - Cloud Vision API enables developers to integrate Google CloudVision detection features within applications including face and landmark detection, image labeling, optical character recognition (OCR), and tagging of explicit content. Prerequisites: 1. Before we begin, we need to set up a project on Google Cloud Developers console (Link: https://console.cloud.google.com/) 2. Enable the Google Cloud Vision API under 'API and Services' 3. Copy the API key under Credentials which looks like this 'AIzaSyCwpab-fbRd6*******ne60NyTkA' 4. Android Studio(3+) with latest SDK Let's try our hands on implementation: Define Permissions in AndroidManifest.xml Create an Activity where request to Text recognition using Google CloudVision API will be processed Define Constants: private static final String CLOUD_VISION_API_KEY =API_KEY; public static final String FILE_NAME = "temp.jpg"; private static final String ANDROID_CERT_HEADER = "X-Android-Cert"; private static final String ANDROID_PACKAGE_HEADER = "X-Android-Package"; private static final int MAX_LABEL_RESULTS = 10; private static final int MAX_DIMENSION = 1200; private static final String TAG = MainActivity.class.getSimpleName(); private static final int GALLERY_PERMISSIONS_REQUEST = 0; private static final int GALLERY_IMAGE_REQUEST = 1; public static final int CAMERA_PERMISSIONS_REQUEST = 2; public static final int CAMERA_IMAGE_REQUEST = 3; Note: CLOUD_VISION_API_KEY is a variable where you'll have to define your API Key copied from Google Cloud developers console. Create a function to start Device Camera public void startCamera() { if (PermissionUtils.requestPermission( this, CAMERA_PERMISSIONS_REQUEST, Manifest.permission.READ_EXTERNAL_STORAGE, Manifest.permission.CAMERA)) { Intent intent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE); Uri photoUri = FileProvider.getUriForFile(this, getApplicationContext().getPackageName() + ".provider", getCameraFile()); intent.putExtra(MediaStore.EXTRA_OUTPUT, photoUri); intent.addFlags(Intent.FLAG_GRANT_READ_URI_PERMISSION); startActivityForResult(intent, CAMERA_IMAGE_REQUEST); } } Read the image captured onActivityResult @Override protected void onActivityResult(int requestCode, int resultCode, Intent data) { super.onActivityResult(requestCode, resultCode, data); if (requestCode == CAMERA_IMAGE_REQUEST && resultCode == RESULT_OK) { Uri photoUri = FileProvider.getUriForFile(this, getApplicationContext().getPackageName() + ".provider", getCameraFile()); uploadImage(photoUri); } } Create functions to uploadImage to Google Cloud Storage for processing public void uploadImage(Uri uri) { if (uri != null) { try { // scale the image to save on bandwidth Bitmap bitmap = scaleBitmapDown( MediaStore.Images.Media.getBitmap(getContentResolver(), uri), MAX_DIMENSION); callCloudVision(bitmap); mMainImage.setImageBitmap(bitmap); } catch (IOException e) { Log.d(TAG, "Image picking failed because " + e.getMessage()); Toast.makeText(this, R.string.image_picker_error, Toast.LENGTH_LONG).show(); } } else { Log.d(TAG, "Image picker gave us a null image."); Toast.makeText(this, R.string.image_picker_error, Toast.LENGTH_LONG).show(); } } private void callCloudVision(final Bitmap bitmap) { // Switch text to loading mImageDetails.setText(R.string.loading_message); // Do the real work in an async task, because we need to use the network anyway try { AsyncTask textDetectionTask = new TextDetectionTask(this, prepareAnnotationRequest(bitmap)); labelDetectionTask.execute(); } catch (IOException e) { Log.d(TAG, "failed to make API request because of other IOException " + e.getMessage()); } } Create an AsyncTask that processes the image for text detection in background thread private Vision.Images.Annotate prepareAnnotationRequest(Bitmap bitmap) throws IOException { HttpTransport httpTransport = AndroidHttp.newCompatibleTransport(); JsonFactory jsonFactory = GsonFactory.getDefaultInstance(); VisionRequestInitializer requestInitializer = new VisionRequestInitializer(CLOUD_VISION_API_KEY) { /** * We override this so we can inject important identifying fields into the HTTP * headers. This enables use of a restricted cloud platform API key. */ @Override protected void initializeVisionRequest(VisionRequest visionRequest) throws IOException { super.initializeVisionRequest(visionRequest); String packageName = getPackageName(); visionRequest.getRequestHeaders().set(ANDROID_PACKAGE_HEADER, packageName); String sig = PackageManagerUtils.getSignature(getPackageManager(), packageName); visionRequest.getRequestHeaders().set(ANDROID_CERT_HEADER, sig); } }; Vision.Builder builder = new Vision.Builder(httpTransport, jsonFactory, null); builder.setVisionRequestInitializer(requestInitializer); Vision vision = builder.build(); BatchAnnotateImagesRequest batchAnnotateImagesRequest = new BatchAnnotateImagesRequest(); batchAnnotateImagesRequest.setRequests(new ArrayList() {{ AnnotateImageRequest annotateImageRequest = new AnnotateImageRequest(); // Add the image Image base64EncodedImage = new Image(); // Convert the bitmap to a JPEG // Just in case it's a format that Android understands but Cloud Vision ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream(); bitmap.compress(Bitmap.CompressFormat.JPEG, 90, byteArrayOutputStream); byte[] imageBytes = byteArrayOutputStream.toByteArray(); // Base64 encode the JPEG base64EncodedImage.encodeContent(imageBytes); annotateImageRequest.setImage(base64EncodedImage); // add the features we want annotateImageRequest.setFeatures(new ArrayList() {{ Feature labelDetection = new Feature(); textDetection.setType("DOCUMENT_TEXT_DETECTION"); add(labelDetection); }}); // Add the list of one thing to the request add(annotateImageRequest); }}); Vision.Images.Annotate annotateRequest = vision.images().annotate(batchAnnotateImagesRequest); // Due to a bug: requests to Vision API containing large images fail when GZipped. annotateRequest.setDisableGZipContent(true); Log.d(TAG, "created Cloud Vision request object, sending request"); return annotateRequest; } private static class TextDetectionTask extends AsyncTask { private final WeakReference mActivityWeakReference; private Vision.Images.Annotate mRequest; TextDetectionTask(MainActivity activity, Vision.Images.Annotate annotate) { mActivityWeakReference = new WeakReference<>(activity); mRequest = annotate; } @Override protected String doInBackground(Object... params) { try { Log.d(TAG, "created Cloud Vision request object, sending request"); BatchAnnotateImagesResponse response = mRequest.execute(); return convertResponseToString(response); } catch (GoogleJsonResponseException e) { Log.d(TAG, "failed to make API request because " + e.getContent()); } catch (IOException e) { Log.d(TAG, "failed to make API request because of other IOException " + e.getMessage()); } return "Cloud Vision API request failed. Check logs for details."; } protected void onPostExecute(String result) { MainActivity activity = mActivityWeakReference.get(); if (activity != null && !activity.isFinishing()) { TextView imageDetail = activity.findViewById(R.id.image_details); imageDetail.setText(result); } } } Create a function that will scale down the bitmap captured to make processing faster private Bitmap scaleBitmapDown(Bitmap bitmap, int maxDimension) { int originalWidth = bitmap.getWidth(); int originalHeight = bitmap.getHeight(); int resizedWidth = maxDimension; int resizedHeight = maxDimension; if (originalHeight > originalWidth) { resizedHeight = maxDimension; resizedWidth = (int) (resizedHeight * (float) originalWidth / (float) originalHeight); } else if (originalWidth > originalHeight) { resizedWidth = maxDimension; resizedHeight = (int) (resizedWidth * (float) originalHeight / (float) originalWidth); } else if (originalHeight == originalWidth) { resizedHeight = maxDimension; resizedWidth = maxDimension; } return Bitmap.createScaledBitmap(bitmap, resizedWidth, resizedHeight, false); } Finally, create a function to display the text extracted from image private static String convertResponseToString(BatchAnnotateImagesResponse response) { String result = ""; TextAnnotation fullTextAnnotation = response.getResponses().get(0).getFullTextAnnotation(); if (fullTextAnnotation != null) { result = fullTextAnnotation.get("text").toString(); } else { result = ""; } return result; } The variable result will contain the text extracted from image. Hope that helps :)

Area Of Work: Computer Vision

Mohd Adnan

30 Apr 2018

Tesseract OCR Working For various operating systems Tesseract work as an optical character recognition engine or system. It is released under the Apache License. ... In the year 2006, Tesseract is open-source software, Tesseract was considered among one of the foremost accurate open-source OCR engines then available.Tesseract is that the hottest and qualitative OCR library.OCR uses an AI system for text search and its recognition on images in different formates.Tesseract engine working is finding templates in letters, pixels, sentences, and words. Tesseract software uses a two-step approach that calls adaptive recognition. Tesseract requires one data stage for character recognition, then the second stage to fulfill any letters, it wasn't insured in, by letters which will match the word or sentence context.Tesseract also has unicode (UTF-8) support, and may able to recognize quite 100 languages "out of the box".Tesseract also supports various output formats like: plain-text, hocr(html), pdf, tsv, invisible-text-only pdf.How Does it WorkTesseract tests the text lines to work out whether or not they are fixed pitch. Where Tesseract finds the fixed-pitch text, further it chops the words into characters using the pitch and then disables the associator and chopper on these words for the word recognition step.Now here is how Tesseract works:The first step may be a connected component analysis during which outlines of the components are stored.This is a computationally expensive design decision at the time, but features a significant advantage: by inspection of the nesting of outlines, and therefore the number of kid and grandchild outlines, it's simple to detect inverse text and recognize it as easily as black-on-white text.Tesseract is perhaps the primary OCR engine ready to handle white-on-black text so trivially. At this point of the stage, the outlines are now combined together, and purely by nesting, into Blobs.Blobs are then organized into text lines in next step, and therefore the regions and lines are analyzed for fixed pitch or proportional text.Text lines are broken into words differently consistent with the type of character spacing. And then Fixed pitch text is now chopped by character cells.And in the next step Proportional text is now then broken into words using definite spaces and fuzzy spaces.Recognition then proceeds as a two-pass process.In the first pass, an effort is formed to acknowledge each word successively . Each word that's satisfactory then passed to an adaptive classifier as a training data. The adaptive classifier then gets an opportunity to more accurately recognize text lower down the page. Since, the adaptive classifier may have learned something useful too late to form a contribution near the highest point of the page, a second pass is run over the page, during which words that weren't recognized tolerably are recognized again. And into the final phase of the process it resolves fuzzy spaces, and checks alternative hypotheses for the x-height to locate small-cap text.

Area Of Work: Computer Vision

Ravi Rose

11 May 2021

Object Detection using DETR Computer vision has considered object detection as a crucial undertaking. Instead of just identifying things in images, this implies that we have to be able to precisely locate them using bounding boxes. Over time, it has typically entailed many complicated and multi-step procedures, which often result in problems such as false alarms and inefficiency. This is where DETR comes in; an acronym for Detection Transformer that brings a new perspective to object detection based on transformer power. In the following paragraphs, we shall see how DETR operates and why it's attracting attention across the globe.What is a DETR?DETR is an objеct dеtеction process fоr transmography with convolutional neural networks (CNNs). Convolutional neural networks (CNNs) are transformers thаt mаke use of аttention mechanism for simultaneous processing of objects rather than sequentiаlly. This helps them tο capture long-range dependencies аnd word- contextual representations by objects without distorted images.It is a collection-based global loss that requires individual predictions via bipartite matching and a transformer encoder-decoder structure. Modern deep learning algorithms make multi-step object detection which points to the problem of false positives. DETR intends to explain this innovatively and efficiently.It handles an object detection difficulty as a close-set prediction problem with the guidance of an encoder-decoder structure based on transformers. By set, I expect the position of bounding boxes.Transformer:It is a structure during transforming one set into another one with the guidance of two parts (Encoder and Decoder), but it differs from the previously reported sequence-to-sequence models because it does not indicate any Recurrent Network (GRU, LSTM, etc.).It relies on a simple yet powerful mechanism calledattention, which allows AI models to selectively focus on certain parts of their input and thus think more effectively.DETR Pipeline:Inference:Calculate the Image feature from the backbone.To Transfer the encoder-decoder structureDetermining a set of predictionsTraining:Calculate Image features from the backboneTo transformer encoder-decoder architecture.Advantages of the DETR Pipeline:Easy to UseNo Custom LayersAn easy extension to other tasksPrior information about anchors or handcrafted algorithms like NMS is not neededDETER Architecture:It Contains three ComponentsCNN backboneextracts compact feature representationencoder-decoder architectureFFN (Feed Forward Network)that makes final detection predictionBackbone:Frequently utilizedResNet50as the backbone of DETR. Ideally, any backbone can be used depending on the complexity of the task. It provides a low-dimensional description of the image must a refined feature.Encoder:The encoder layer has a fixed architecture and consists ofa multi-head attention module and anFNN.Decoder:Itsupports the conventional structure of the transformer, transforming N embedding of size d using multi-head self and encoding decoder recognition mechanism. The difference with the first transformer is that the DETR model decodes the N object similarly to any decode layer.FFN:It predicts thenormalized core coordinates, length & breadth of the box w.r.t input image, and the linear layer predictsclass label using a softmax function.Result:for more detailsclick here.

Area Of Work: Computer Vision

Pradeep Farthyal

01 Sep 2020

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