Smartphones Are Becoming More Preferred Companions To Users Than Desktops Or Notebooks. Knowing That Smartphones Are Most Popular With Users At The Age Around 26, Using Smartphones To Speed Up The Process Of Taking Attendance By University Instructors Would Save Lecturing Time And Hence Enhance The Educational Process. This Paper Proposes A System That Is Based On A BAR Code, Which Is Being Displayed For Students During Or At The Beginning Of Each Lecture. The Students Will Need To Scan The Code In Order To Confirm Their Attendance. The Paper Explains The High Level Implementation Details Of The Proposed System. It Also Discusses How The System Verifies Student Identity To Eliminate False Registrations.
Biometrics With Facial Recognition Is Now Widely Used. A Face Identification System Should Identify Not Only Someone's Faces But Also Detect Spoofing Attempts With Printed Face Or Digital Presentations. A Sincere Spoofing Prevention Approach Is To Examine Face Liveness As Eye Blinking. Nevertheless, This Approach Is Helpless When Dealing With Video-based Replay Attacks. For This Reason, This Paper Proposes A Combined Method Of Face Liveness Detection And CNN (Convolutional Neural Network) Classifier. The Test Results Show That The Module Created Can Recognize Various Kinds Of Facial Spoof Attacks, Such As Using Posters, Masks, Or Smartphones And Second Step Verification. Then Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., HOG Algorithm To Record The Student Attendance. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. ASAS Marks Individual Attendance, If They Image Matches The Image In The Database. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence.
This Study Is An Attempt To Understand And Address The Mental Health Issue, Of Working Professionals Through Facial Expression Recognition And Suggest A List Of Appropriate Songs That Can Improve His Mood. A Brief Search Was Conducted On How Music Can Affect The User Mood In Short-term To Gain Knowledge And Enable Us To Provide The Users With A List Of Music Tracks That Work Well On Improving The User Moods. As A Society, We Are All Currently Talking About Ways As To How A Person Who Is Suffering From Any Emotional Issue Can Adopt Certain Ways To Come Out Of A Specific Circumstance And How We As A Society Can Support Such People In These Situations. Our Endeavor Is To Work On A Way Where The Identification Of Such Persons Who Are Going Through A Difficult Phase In Their Life Can Be Performed. Our Endeavor Is To Work On A Way Where The Identification Of Such Persons Who Are Going Through A Difficult Phase In Their Life Can Be Performed. It Is Not Always Evident That A Person Going Through A Tough Phase May Open Up About Their Feelings To People Around Them And Hence Making Use Of AI/ML To Identify A Person’s Emotion Through Their Facial Expressions Captured Over A Span Of Time Thereby Recommending Them Some Activities, Thoughts Which Can Help Them In Getting Over Their Emotions When They Are Sad, Fearful It Suggestion The Music.
A System That Takes Down Students Attendance Using Qr Code. Every Student Is Provided With A Card Containing A Unique Qr Code. Students Just Have To Scan Their Cards In Front Of Webcam And The System Notes Down Their Attendance As Per Dates. Each Qr Code Contains A Unique Id For Students. System Then Stores All The Students’ Attendance Records And Generates Defaulter List. It Also Generates An Overall Report In Excel Sheet For Admin. Such Type Of Application Is Very Useful In School As Well As In College For Daily Attendance.
Women And Girls Have Been Experiencing A Lot Of Violence And Harassment In Public Places In Various Cities Starting From Stalking And Leading To Sexual Harassment Or Sexual Assault. This Research Paper Basically Focuses On The Role Of Social Media In Promoting The Safety Of Women In Indian Cities With Special Reference To The Role Of Social Media Websites And Applications Including Twitter Platform Facebook And Instagram. This Paper Also Focuses On How A Sense Of Responsibility On Part Of Indian Society Can Be Developed The Common Indian People So That We Should Focus On The Safety Of Women Surrounding Them. Tweets On Twitter Which Usually Contains Images And Text And Also Written Messages And Quotes Which Focus On The Safety Of Women In Indian Cities Can Be Used To Read A Message Amongst The Indian Youth Culture And Educate People To Take Strict Action And Punish Those Who Harass The Women. Twitter And Other Twitter Handles Which Include Hash Tag Messages That Are Widely Spread Across The Whole Globe Sir As A Platform For Women To Express Their Views About How They Feel While We Go Out For Work Or Travel In A Public Transport And What Is The State Of Their Mind When They Are Surrounded By Unknown Men And Whether These Women Feel Safe Or Not?
Handwritten Signature Recognition Is An Important Behavioral Biometric Which Is Used For Numerous Identification And Authentication Applications. There Are Two Fundamental Methods Of Signature Recognition, On-line Or Off-line. On-line Recognition Is A Dynamic Form, Which Uses Parameters Like Writing Pace, Change In Stylus Direction And Number Of Pen Ups And Pen Downs During The Writing Of The Signature. Off-line Signature Recognition Is A Static Form Where A Signature Is Handled As An Image And The Author Of The Signature Is Predicted Based On The Features Of The Signature. The Current Method Of Off-line Signature Recognition Predominantly Employs Template Matching, Where A Test Image Is Compared With Multiple Specimen Images To Speculate The Author Of The Signature. This Takes Up A Lot Of Memory And Has A Higher Time Complexity. This Paper Proposes A Method Of Off-line Signature Recognition Using Convolution Neural Network. The Purpose Of This Paper Is To Obtain High Accuracy Multi-class Classification With A Few Training Signature Samples. Images Are Preprocessed To Isolate The Signature Pixels From The Background/noise Pixels Using A Series Of Image Processing Techniques. Initially, The System Is Trained With 27 Genuine Signatures Of 10 Different Authors Each. A Convolution Neural Network Is Used To Predict A Test Signature Belongs To Which Of The 10 Given Authors. Different Public Datasets Are Used To Demonstrate Effectiveness Of The Proposed Solution.
Our Country, India Is The Largest Democratic Country In The World. So It Is Essential To Make Sure That The Governing Body Is Elected Through A Fair Election. India Has Only Offline Voting System Which Is Not Effective And Up To The Mark As It Requires Large Man Force And It Also Requires More Time To Process And Publish The Results. Therefore, To Be Made Effective, The System Needs A Change, Which Overcomes These Disadvantages. The New Method Does Not Force The Person's Physical Appearance To Vote, Which Makes The Things Easier. This Paper Focuses On A System Where The User Can Vote Remotely From Anywhere Using His/her Computer Or Mobile Phone And Doesn’t Require The Voter To Get To The Polling Station Through Two Step Authentication Of Face Recognition And OTP System. This Project Also Allows The User To Vote Offline As Well If He/she Feels That Is Comfortable. The Face Scanning System Is Used To Record The Voters Face Prior To The Election And Is Useful At The Time Of Voting. The Offline Voting System Is Improvised With The Help Of RFID Tags Instead Of Voter Id. This System Also Enables The User The Citizens To See The Results Anytime Which Can Avoid Situations That Pave Way To Vote Tampering.
In The Human Body, The Face Is The Most Crucial Factor In Identifying Each Person As It Contains Many Vital Details. There Are Different Prevailing Methods To Capture Person's Presence Like Biometrics To Take Attendance Which Is A Time-consuming Process. This Paper Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., HOG Algorithm To Record The Student Attendance. HOG(Histogram Of Oriented Gradients) Is One Among The Methods And Is Popular As Well As Effective Technique Used For The Image Representation And Classification And It Was Chosen For Its Robustness To Pose And Illumination Shifts. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. The Database Is Updated Upon The Enrolment Of The Student Using An Automation Process That Also Includes Name And Rolls Number. ASAS Marks Individual Attendance, If The Captured Image Matches The Image In The Database I.e., If Both Images Are Identical. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence.
This Study Is An Attempt To Understand And Address The Mental Health Issue, Of Working Professionals Through Facial Expression Recognition. As A Society, We Are All Currently Talking About Ways As To How A Person Who Is Suffering From Any Emotional Issue Can Adopt Certain Ways To Come Out Of A Specific Circumstance And How We As A Society Can Support Such People In These Situations. Our Endeavor Is To Work On A Way Where The Identification Of Such Persons Who Are Going Through A Difficult Phase In Their Life Can Be Performed.
Health Monitoring Is An Important Parameter To Determine The Health Status Of A Person. Measuring The Heart Rate Is An Easy Way To Gauge Our Health. Normal Heart Rate May Vary From Person To Person And A Usually High Or Low Resting Heart Rate Can Be A Sign Of Trouble. There Are Several Methods For The Measurement Of Heart Rate Monitoring Such As Ecg, Ppg Etc. Such Methods Having A Disadvantage That These Are Invasive And Have A Continuous Contact With The Human Body. In Order To Overcome This Problem A New System Is Proposed Using Camera. In This Method A Blind Source Separation Algorithm Is Used For Extracting The Heart Rate Signal From The Face Image. Viola Jones Based Face Detection Algorithm Is Used To Track The Face. FastICA Algorithm Is Exploited To Separate Heart Rate Signal From Noise And Artefacts. Machine Learning Algorithm Is Implemented To Standardize The Signal. The Data Is Successfully Tested With Real Time Video.
Smart Home Is One Application Of The Pervasive Computing Branch Of Science. Three Categories Of Smart Homes, Namely Comfort, Healthcare, And Security. The Security System Is A Part Of Smart Home Technology That Is Very Important Because The Intensity Of Crime Is Increasing, Especially In Residential Areas. The System Will Detect The Face By The Webcam Camera If The User Enters The Correct Password. Face Recognition Will Be Processed By The Raspberry Pi 3 Microcontroller With The Principal Component Analysis Method Using OpenCV And Python Software Which Has Outputs, Namely Actuators In The Form Of A Solenoid Lock Door And Buzzer. The Test Results Show That The Webcam Can Perform Face Detection When The Password Input Is Successful, Then The Buzzer Actuator Can Turn On When The Database Does Not Match The Data Taken By The Webcam Or The Test Data And The Solenoid Door Lock Actuator Can Run If The Database Matches The Test Data Taken By The Sensor. Webcam. The Mean Response Time Of Face Detection Is 1.35 Seconds.
The Real-time Sign Language Recognition System Is Developed For Recognising The Gestures Of Indian Sign Language (ISL). Generally, Sign Languages Consist Of Hand Gestures And Facial Expressions. For Recognising The Signs, The Regions Of Interest (ROI) Are Identified And Tracked Using The Skin Segmentation Feature Of OpenCV. The Training And Prediction Of Hand Gestures Are Performed By Applying Fuzzy C-means Clustering Machine Learning Algorithm. The Gesture Recognition Has Many Applications Such As Gesture Controlled Robots And Automated Homes, Game Control, Human-Computer Interaction (HCI) And Sign Language Interpretation. The Proposed System Is Used To Recognize The Real-time Signs. Hence It Is Very Much Useful For Hearing And Speech Impaired People To Communicate With Normal People.
While Recognizing Any Individual, The Most Important Attribute Is Face. It Serves As An Individual Identity Of Everyone And Therefore Face Recognition Helps In Authenticating Any Person’s Identity Using His Personal Characteristics. The Whole Procedure For Authenticating Any Face Data Is Sub-divided Into Two Phases, In The First Phase, The Face Detection Is Done Quickly Except For Those Cases In Which The Object Is Placed Quite Far, Followed By This The Second Phase Is Initiated In Which The Face Is Recognized As An Individual. Then The Whole Process Is Repeated Thereby Helping In Developing A Face Recognition Model Which Is Considered To Be One Of The Most Extremely Deliberated Biometric Technology. Basically, There Are Two Type Of Techniques That Are Currently Being Followed In Face Recognition Pattern That Is, The Eigenface Method And The Fisherface Method. The Eigenfacemethod Basically Make Use Of The PCA (Principal Component Analysis) To Minimize The Face Dimensional Space Of The Facial Features. The Area Of Concern Of This Paper Is Using The Digital Image Processing To Develop A Face Recognition System.
Communications Is Facial Expression Recognition, As In Non-verbal Communication, Facial Expressions Are Key. In The Field Of Artificial Intelligence, Facial Expression Recognition (FER) Is An Active Research Area, With Several Recent Studies Using Convolutional Neural Networks (Emotions Are A Powerful Tool In Communication And One Way That Humans Show Their Emotions Is Through Their Facial Expressions. One Of The Challenging And Powerful Tasks In Social CNNs). In This Paper, We Demonstrate The Classification Of FER Based On Static Images, Using CNNs, Without Requiring Any Pre-processing Or Feature Extraction Tasks. The Paper Also Illustrates Techniques To Improve Future Accuracy In This Area By Using Preprocessing, Which Includes Face Detection And Illumination Correction. Feature Extraction Is Used To Extract The Most Prominent Parts Of The Face, Including The Jaw, Mouth, Eyes, Nose, And Eyebrows. Furthermore, We Also Discuss The Literature Review And Present Our CNN Architecture, And The Challenges Of Using Max-pooling And Dropout, Which Eventually Aided In Better Performance. We Obtained A Test Accuracy Of 61.7% On FER2013 In A Seven-classes Classification Task Compared To 75.2% In State-of-the-art Classification
Emotion Recognition From Speech Signals Is An Important But Challenging Component Of Human-Computer Interaction (HCI). In The Literature Of Speech Emotion Recognition (SER), Many Techniques Have Been Utilized To Extract Emotions From Signals, Including Many Well-established Speech Analysis And Classification Techniques. Deep Learning Techniques Have Been Recently Proposed As An Alternative To Traditional Techniques In SER. This Paper Presents An Overview Of Deep Learning Techniques And Discusses Some Recent Literature Where These Methods Are Utilized For Speech-based Emotion Recognition. The Review Covers Databases Used, Emotions Extracted, Contributions Made Toward Speech Emotion Recognition And Limitations Related To It.
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