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Facial Recognition is Creeping into Daily Life

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This is my talk from the Scenic City Tech Conference

Facial recognition is everywhere from Facebook to security, gaming, stores, airports, etc. and its use is only growing. Facial recognition is popular because face images exist of almost everyone. You've got driver's license photos, identity badges from wherever you work, library cards, warehouse club cards, social media, and the list goes on. The FBI has said that by 2016, its database will include at least 4.3 million "civil images" — those taken for non-criminal purposes. With the advent of several facial recognition APIs and the innovation leader, Amazon, throwing its hat in the ring with Amazon Rekognize, the technology will become even more common place than it is today. Attend this talk to learn about advances in facial recognition and what it means for your life 1 year, 5 year, and even 10 years from now. After the talk, take a peek behind the scenes at an application that uses facial recognition.

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Facial Recognition is Creeping into Daily Life

  1. 1. FACIAL RECOGNITION IS CREEPING INTO DAILY LIFE Kesha E. Williams @KeshaWillz
  2. 2. PAPER WASP (POLISTES FUSCATUS)
  3. 3. AT FIRST GLANCE, THIS UPSIDE-DOWN FACE APPEARS NORMAL WARREN GOLDSWAIN/SHUTTERSTOCK, ADAPTED BY E. FELICIANO
  4. 4. BUT FLIP IT RIGHT SIDE UP, AND YOU’LL SEE THAT YOUR FACE-ADEPT BRAIN HAS DUPED YOU WARREN GOLDSWAIN/SHUTTERSTOCK, ADAPTED BY E. FELICIANO
  5. 5. THIS TALK • Industry Uses of Facial Recognition • Technical Components • Capabilities of Consumer APIs • Case Study • Security & Privacy Concerns • Future of Facial Recognition
  6. 6. Kesha Williams Software Engineer 20+ ABOUT ME Kesha Williams Software Engineer 20+
  7. 7. COMPUTER VISION • Field of Artificial Intelligence (AI) and Computer Science • Extracts information and attributes from an image • Gives computers a visual understanding of the world
  8. 8. SUBDOMAINS OF COMPUTER VISION Facial Recognition Face Comparison Digital Recognition Object & Scene Detection Image Analysis Demographic Data Abstraction Sentiment Analysis Event Detection Video Tracking Scene Reconstruction
  9. 9. GOAL OF COMPUTER VISION InformationProcessing ANALYSIS DECISION CAPTURE Computer Makes Decision Based on What it Sees
  10. 10. FACIAL RECOGNITION
  11. 11. FACIAL COMPARISON IN TECH TERMS 0 3 4 1 1 1 1 0 0 0 2 2 1 1 1 Face Feature Vector Feature Extraction X = [x1…………xN]
  12. 12. FACEPRINT Thumbprint for Your Face
  13. 13. FACEBOOK 98%
  14. 14. SNAPCHAT
  15. 15. AIRLINE INDUSTRY Forget the boarding pass! JetBlue will just scan your face. Delta to test face scanning baggage check kiosks.
  16. 16. CASINOS
  17. 17. AMAZON GO
  18. 18. PAYMENTS: CREDIT CARD Selfie Pay
  19. 19. HOTELS System to identify VIP guests who could be greeted by name by hotel staff
  20. 20. CIVIL IMAGES FBI 4.3 Million+ “Civil” Images
  21. 21. FBI 2012
  22. 22. U.S. CUSTOMS Biometric Exit Program
  23. 23. NSA Global Surveillance Operations
  24. 24. DMV 25 Years Later
  25. 25. CAPABILITIES OF CONSUMER APIS
  26. 26. OBJECT & SCENE DETECTION & SCENE DETECTION IMAGES COURTESY OF AMAZON WEB SERVICES
  27. 27. OBJECT & SCENE LABELS
  28. 28. OBJECT & SCENE JSON { "Labels": [ { "Confidence": 99.25341796875, "Name": "Skateboard" }, { "Confidence": 99.25341796875, "Name": "Sport" }, { "Confidence": 99.24723052978516, "Name": "People" }, { "Confidence": 99.24723052978516, "Name": "Person" }, { "Confidence": 99.23908233642578, "Name": "Human" }, { "Confidence": 97.42487335205078, "Name": "Parking" },…..
  29. 29. FACIAL ANALYSIS IMAGES COURTESY OF AMAZON WEB SERVICES
  30. 30. FACIAL ANALYSIS RESULTS
  31. 31. FACIAL ANALYSIS RESULTS JSON { "FaceDetails": [ { "AgeRange": { "High": 38, "Low": 23 }, "Beard": { "Confidence": 97.11119842529297, "Value": false }, "BoundingBox": { "Height": 0.42500001192092896, "Left": 0.1433333307504654, "Top": 0.11666666716337204, "Width": 0.2822222113609314 }, "Confidence": 99.8899917602539, "Emotions": [ { "Confidence": 93.29251861572266, "Type": "HAPPY" }, { "Confidence": 28.57428741455078, "Type": "CALM" }, { "Confidence": 1.4989674091339111, "Type": "ANGRY" } ],
  32. 32. FACIAL COMPARISON Reference Face Comparison Faces IMAGES COURTESY OF AMAZON WEB SERVICES
  33. 33. FACIAL COMPARISON RESULTS IMAGES COURTESY OF AMAZON WEB SERVICES
  34. 34. FACIAL COMPARISON JSON { "FaceMatches": [ { "Face": { "BoundingBox": { "Height": 0.07888888567686081, "Left": 0.34166666865348816, "Top": 0.185555562376976, "Width": 0.11833333224058151 }, "Confidence": 99.99418640136719, "Landmarks": [ { "Type": "eyeLeft", "X": 0.3799784481525421, "Y": 0.21625632047653198 }, { "Type": "eyeRight", "X": 0.4214431047439575, "Y": 0.21468327939510345 }, { "Type": "nose", "X": 0.39977848529815674, "Y": 0.22858485579490662 ….."Quality": { "Brightness": 36.6235466003418, "Sharpness": 99.47134399414062 } }, "Similarity": 98 }
  35. 35. REAL LIFE COMPARISON 97% SIMILARITY
  36. 36. REAL LIFE COMPARISON 49% SIMILARITY
  37. 37. REAL LIFE COMPARISON 82% SIMILARITY
  38. 38. REAL LIFE COMPARISON 43% SIMILARITY
  39. 39. REAL LIFE COMPARISON 9% SIMILARITY
  40. 40. INDEX FACES & SEARCH FACES convert images into image vectors and associate with a FaceID for searching Index Search Collection
  41. 41. S. A. M. (SUSPICIOUS ACTIVITY MONITOR) www.iamsam.tech
  42. 42. facial_analysis_response = rekognition.detect_faces(Image={"Bytes": imagebytes}, Attributes=['ALL']) object_scene_response = rekognition.detect_labels(Image={"Bytes": imagebytes}) # if not a photo of a person, tweet user stating "not a photo of a human" if is_human(object_scene_response): isHappy = determine_happy(facial_analysis_response, most_recent_mention) # if not happy, continue on if isHappy == False: #get age as average average_age = (facial_analysis_response['FaceDetails'][0]['AgeRange']['High'] + facial_analysis_response['FaceDetails'][0]['AgeRange']['Low'])/2 # get gender gender = facial_analysis_response['FaceDetails'][0]['Gender']['Value'] PYTHON CODE SNIPPET
  43. 43. PYTHON CODE SNIPPET def determine_happy(facial_analysis_response, most_recent_mention): is_happy = False # loop through emotions and get emotional state for index,state in enumerate(facial_analysis_response['FaceDetails'][0]['Emotions']): if state['Type'] == 'HAPPY' and state['Confidence'] > 70: client.update_status(status='@' + most_recent_mention['user']['screen_name'] + " This person appears happy. Happy people don't steal.", in_reply_to_status_id=most_recent_mention["id"]) is_happy = True break return is_happy
  44. 44. SECURITY & PRIVACY CONCERNS
  45. 45. BILLS CIRCULATING IN STATE LEGISLATURES Illinois Biometric Information Privacy Act (BIPA) Alaska (HB 72) Connecticut (Proposed HB 5522) Montana (HB 518)New Hampshire (HB 523) Washington (HB 1493-S) Connecticut (Proposed HB 5522) Montana (HB 518)
  46. 46. HIDING IN PLAIN SITE Changing Your Pixel Pattern
  47. 47. PRIVACY VISORS
  48. 48. PRIVACY VISORS
  49. 49. PRIVACY MAKEUP
  50. 50. FUTURE OF COMPUTER VISION
  51. 51. ANONYMITY IS GONE • Your civil image exists in a government database • Be mindful that you can be identified “What I hate is the loss of anonymity.” --Harrison Ford
  52. 52. CONTACT http://www.kesha.tech kesha@s4technology.com 678-364-2767 @KeshaWillz
  53. 53. ? QUESTIONS

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