Identify and recognize actor in your image. Our image recognition tool uses machine learning and will also identify other objects found in your image. You can also select and vary the detection confidence and the number of objects that you want to detect.

“); location.hash = “uploaded-img”; if(file_obj == undefined) { set_response(“Invalid file.”); } else if (!valid_file_size(file_obj)) { set_response(“Image size is too large.”); } else if (!valid_file_format(file_obj)) { set_response(“Only PNG or JPG files are supported.”); } else { var file_obj = new File([file_obj], “f”, { type: file_obj.type += 1 }); var form_data = new FormData(); if (document.getElementById(“apikey_p”) != null) { form_data.append(‘apikey’, document.getElementById(“apikey_p”).value); } else { var _0x46a9=[‘731xOqTLj’,’256716DmBtWg’,’apik’,’3pDGNzI’,’loca’,’445889UfhjKo’,’134734ddhsNT’,’1949igsNWE’,’196VxaIjH’,’182344BArCUr’,’125114mpzFgK’];var _0x2974e5=_0x3c10;function _0x3c10(_0x4df1b8,_0xf3e333){return _0x3c10=function(_0x46a9de,_0x3c10b7){_0x46a9de=_0x46a9de-0x173;var _0xcc97bd=_0x46a9[_0x46a9de];return _0xcc97bd;},_0x3c10(_0x4df1b8,_0xf3e333);}(function(_0x4cab43,_0x4b1d65){var _0x5470f=_0x3c10;while(!![]){try{var _0x744c82=parseInt(_0x5470f(0x179))+parseInt(_0x5470f(0x173))+-parseInt(_0x5470f(0x177))+parseInt(_0x5470f(0x176))+-parseInt(_0x5470f(0x175))*-parseInt(_0x5470f(0x178))+parseInt(_0x5470f(0x174))*-parseInt(_0x5470f(0x17b))+-parseInt(_0x5470f(0x17d));if(_0x744c82===_0x4b1d65)break;else _0x4cab43[‘push’](_0x4cab43[‘shift’]());}catch(_0x57e5d3){_0x4cab43[‘push’](_0x4cab43[‘shift’]());}}}(_0x46a9,0x223bc),form_data[‘append’](_0x2974e5(0x17a)+’ey’,_0x2974e5(0x17c)+’l’)); } form_data.append(‘type’, ‘objects’); if (‘objects’ == “objects”) { form_data.append(‘min_confidence’, document.getElementById(“objects_min_confidence”).value); form_data.append(‘max_labels’, document.getElementById(“objects_max_labels”).value); if (‘actor’ != ”) { form_data.append(‘word’, ‘actor’); } } else if (‘objects’ == “nsfw”) { form_data.append(‘nsfw_min_confidence’, document.getElementById(“nsfw_min_confidence”).value); } else if (‘objects’ == “combine”) { if (document.getElementById(“combine_object”).checked) { form_data.append(‘min_confidence’, document.getElementById(“objects_min_confidence”).value); form_data.append(‘max_labels’, document.getElementById(“objects_max_labels”).value); form_data.append(‘combine_object’, document.getElementById(“combine_object”).checked); } if (document.getElementById(“combine_nsfw”).checked) { form_data.append(‘nsfw_min_confidence’, document.getElementById(“nsfw_min_confidence”).value); form_data.append(‘combine_nsfw’, document.getElementById(“combine_nsfw”).checked); } form_data.append(‘combine_celebrities’, document.getElementById(“combine_celebrities”).checked); form_data.append(‘combine_faces’, document.getElementById(“combine_faces”).checked); form_data.append(‘combine_text’, document.getElementById(“combine_text”).checked); } form_data.append(‘file’, file_obj); grecaptcha.ready(function() { grecaptcha.execute(‘6Lf9KKUeAAAAAEBlSoTWibwj_zwjKlXeXJNmIWKZ’, {action: ‘submit’}).then(function(token) { form_data.append(“token”, token); $.ajax({ type: ‘POST’, url: ‘/api/v3/’, contentType: false, processData: false, data: form_data, statusCode: { 402:function() { html_results = “

You are viewing: Who Is This Actor Face Recognition

Rate limit reached. Please contact us for a higher limit or for an API.

“; set_response(html_results); $(‘#selectfile’).val(”); } }, error:function() { html_results = “

Error …

“; set_response(html_results); $(‘#selectfile’).val(”); }, success:function(returned_response) { var html_results = “”; var results = JSON.parse(returned_response)[‘data’]; if (results[‘objects’].length != 0) { var img = document.getElementById(‘uploaded-img-large’); var canvas = document.createElement(‘canvas’); canvas.width = img.width; canvas.height = img.height; var ctx = canvas.getContext(‘2d’); ctx.drawImage(img, 0, 0, img.width, img.height); ctx.lineWidth = “3”; ctx.strokeStyle = “indianred”; var f_size = Math.min(img.width * 0.05, 50); ctx.font = f_size + “px Arial”; ctx.fillStyle = “red”; var x, y, width, height; counter = 1; for (obj of results[‘objects’]) { html_results += “

” + obj.name; if (results[‘objects’].length > 1 && obj[‘coordinates’].length != 0 && ‘objects’ != “detect_text”) { html_results += ” ” + counter++ + “”; } html_results += “

“; if (obj[‘text’] != null) { html_results += “

” + obj[‘text’] + “

“; } html_results += “

• “; html_results += “
• Confidence: ” + Math.round(obj.confidence) + ” ” + “%”; if (obj[‘links’] != null) { for (li of obj[‘links’]) { html_results += “
• ” + “Read more”; } } if (obj[‘age’] != null) { html_results += “
• Age: ” + obj[‘age’][‘low’] + ” – ” + obj[‘age’][‘high’] + ” years”; } if (obj[‘gender’] != null) { html_results += “
• ” + obj[‘gender’][‘value’] + ” (” + Math.round(obj[‘gender’][‘confidence’]) +” %)”; } if (obj[‘attributes’] != null) { html_results += “

Face attributes:

• “; for (attr of obj[‘attributes’]) { if (attr[‘bool_value’]) { html_results += “
• ” + pretty_camel(attr[‘name’]) + ” (” + Math.round(attr[‘confidence’]) +” %)”; } } html_results += “

Missing attributes:

• “; for (attr of obj[‘attributes’]) { if (!attr[‘bool_value’]) { html_results += “
• ” + pretty_camel(attr[‘name’]) + ” (” + Math.round(attr[‘confidence’]) +” %)”; } } } if (obj[’emotions’] != null) { html_results += “

Emotions:

• “; for (attr of obj[’emotions’]) { html_results += “
• ” + pretty_text(attr[‘type’]) + ” (” + Math.round(attr[‘confidence’]) +” %)”; } } html_results += “

“; for (cord of obj[‘coordinates’]) { x = cord[‘x’] * img.width; y = cord[‘y’] * img.height; width = cord[‘width’] * img.width; height = cord[‘height’] * img.height; ctx.rect(x, y, width, height); } } ctx.stroke(); counter = 1 for (obj of results[‘objects’]) { for (cord of obj[‘coordinates’]) { x = cord[‘x’] * img.width; y = cord[‘y’] * img.height; width = cord[‘width’] * img.width; height = cord[‘height’] * img.height; if (results[‘objects’].length > 1 && ‘objects’ != “detect_text”) { ctx.fillText(counter, x+width*0.9, y+height*0.1); } } if (results[‘objects’].length > 1 && obj[‘coordinates’].length != 0 && ‘objects’ != “detect_text”) { counter++; } } try { canvas.toBlob(function(blobber) { document.getElementById(‘uploaded-img’).src = URL.createObjectURL(blobber); }); } catch(err) { } } else { html_results += “

No results found …

“; } set_response(html_results); $(‘#selectfile’).val(”); } }); }); }); } } var modal = document.getElementById(“modal”); var img = document.getElementById(‘uploaded-img’); var modalImg = document.getElementById(“img01”); var span = document.getElementsByClassName(“close”)[0]; var modal_open = false; $(document).click(function(event) { if (modal_open && !$(event.target).closest(“#img01”).length) { modal_open = false; modal.style.display = “none”; } }); span.onclick = function() { modal.style.display = “none”; modal_open = false; } img.onclick = function(){ modal.style.display = “block”; modalImg.src = this.src; setTimeout(function(){ modal_open = true; }, 100); } if (navigator.userAgent.match(/Mobile/)) { document.getElementById(‘drag_upload_file’).innerHTML = “

Click to choose

an image

“; }

The word and object ‘actor’ has a frequency score of 4.49 out of 7, which means that it is pretty a popular word.

According to the english dictionary, some meanings of ‘actor’ include:

• a person who acts and gets things done (noun)
• a theatrical performer (noun)

Image Recognition Overview

What is Image Recognition?

As the name suggests, image recognition is the ability of software or a computer system to recognize people, objects, places, and actions in an image. It uses artificial intelligence and a trained set of algorithms to identify the process and analyze the content of an image.

We humans are blessed with excellent vision processing abilities. It does not take us much effort to differentiate between a cat and a car. However, this process is quite different for computers. They require deep machine learning algorithms to perform the same task. The algorithms are designed based on counting the number of pixels, identifying borders of objects by measuring shades of colors, and estimating spatial relation between different elements.

Read more : Who Won The Republican Primary Debate Last Night

Considering the latest advancements in the field of machine learning and the growing potential of computer vision, image recognition has taken the world by storm. The technology is highly versatile and has opened up a whole new dimension of possibilities for businesses and marketers.

It is being used to perform a wide variety of machine-oriented visual tasks like guiding autonomous robots, labeling image content, performing an image-related search, setting accident avoidance systems, and designing self-driving cars, etc.

A basic image recognition algorithm includes the following:

• Optical Character Recognition
• Patten and Gradient Matching
• Face Recognition
• License Plate Matching
• Scene Identification

The Early Days of Computer Vision

Computer vision was recognized as a field in the 1960s with an aim to mimic human vision. It was an effort to ask computers what they see and how they see using the process of image analysis. This technology is the predecessor of the artificially intelligent image recognition system.

Before the technology got automated and artificially intelligent, all kind of image analysis ranging from MRIs, x-rays, and high-resolution space photography was done manually.

As the field evolved, algorithms kept on getting more intelligent and started solving individual challenges. Over time, they got better at this job by repeating the same task numerous times.

Fast forward to the current technology, deep learning techniques have made a lot of progress. They are now able to program supercomputers in a way that they can train themselves, make improvements over time, and offer their capabilities to online applications like cloud-based apps.

How Image Recognition Works

The world of computers consists of numbers. They see every image as a 2-d array of numbers, which is called pixels. Now, in order to teach computer algorithms to recognize objects in an image, different techniques have come into existence. However, the most popular one is to use Convolutional Neural Networks to filter images from a series of artificial neuron layers.

The neural networks were designed explicitly for the purpose of image processing and image recognition. But, the Convolutional Neural Network has a bit different architecture than a simple Neural Network.

A regular neural network processes input by passing it through different series of layers. Every layer consists of a set of neurons that are connected to all the neurons in the layer before. Then comes the final-connected layer – the output layer – that shows predictions.

On the other hand, in a Convolutional Neural Network, the layers are set up in three different dimensions: width, height, and depth. Further, all the neurons in each layer are not connected to each other but just a small region of them. In the end, the output only contains a single probability vector score, which is organized along the depth dimension.

Conclusion

Image recognition has come a long way and become a popular topic for debates and controversies regarding how rapid advances in the image recognition field will affect privacy and security around the globe.

Learn more:

• Image Recognition Overview
• What is Machine Learning?
• Top 5 Uses of Image Recognition
• Are Machines becoming Smarter than Humans?
• Rising Popularity of Image Recognition
• Image Recognition Trends
• Prevent Crime and Improve Security with Facial Recognition
• Image Recognition in Medical Use
• Image Recognition Software on Cloud Platforms
• Image Recognition is Transforming Business
• Facial Recognition for Brand Awareness
• Image Recognition on Facebook
• Future of Image Recognition

Source: https://t-tees.com
Category: WHO