Machine Analysis of Facial Expressions

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Modeling of Human Upper Body for Sign Language Recognition

Sign Language Recognition systems require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. Head, face, forehead, shoulders and chest are very crucial parts that can carry a lot of positioning information of hand gestures in gesture classification. In this paper as the main contribution, a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. Scaling the extracted parts during body orientation was attained using partial estimation of face size. Tracking the extracted parts for front and side view was achieved using CAMSHIFT [24]. The outcome of the system makes it applicable for real-time applications such as Sign Languages Recognition (SLR) systems

Modeling of human upper body for sign language recognition

Sign Language Recognition systems require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. Head, face, forehead, shoulders and chest are very crucial parts that can carry a lot of positioning information of hand gestures in gesture classification. In this paper as the main contribution, a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. Scaling the extracted parts during body orientation was attained using partial estimation of face size. Tracking the extracted parts for front and side view was achieved using CAMSHIFT [24]. The outcome of the system makes it applicable for real-time applications such as Sign Languages Recognition (SLR) systems. Keywords: Human upper body detectio

Sequential Feature Selection Using Hybridized Differential Evolution Algorithm and Haar Cascade for Object Detection Framework

Intelligent systems an aspect of artificial intelligence have been developed to improve satellite image interpretation with several foci on object-based machine learning methods but lack an optimal feature selection technique. Existing techniques applied to satellite images for feature selection and object detection have been reported to be ineffective in detecting objects. In this paper, differential Evolution (DE) algorithm has been introduced as a technique for selecting and mapping features to Haarcascade machine learning classifier for optimal detection of satellite image was acquired, pre-processed and features engineering was carried out and mapped using adopted DE algorithm. The selected feature was trained using Haarcascade machine learning algorithm. The result shows that the proposed technique has performance Accuracy of 86.2%, sensitivity 89.7%, and Specificity 82.2% respectively

Smile Detector Based on the Motion of Face Reference Points

Inimese ja arvuti suhtlus on kahtlemata tänapäeva ühiskonna väga tähtis osa. Et seda veelgi parandada on võimalik luua süsteeme, kus arvuti reageerib inimese liigutustele või näoilmetele. Naeratamine on ilmselt näoilme, mis annab inimese kohta kõige rohkem informatsiooni. Selles lõputöös kirjeldame algoritmi, mis suudab tuvastada seda, kui inimene naeratab. Selleks leiame kõigepealt Viola-Jones'i algoritmi abil näo asukoha. Seejärel leiame vajalikele näoosadele vastavad kontrollpunktid ning jälgime nende liikumist järgmiste videokaadrite jooksul. Tuvastatud liikumise järgi otsustab algoritm, kas inimene naeratab või mitte.Human and computer interaction is without doubt a really important part of our modern society. In order to improve it even further it is possible to develop computer systems that react to gestures or facial expressions of its user. Smiling is an expression that gives probably the most information about a person. In this thesis we describe an algorithm that understands when a person is smiling. To achieve that we first detect a face of a person using the Viola-Jones algorithm. After that several facial reference points are located and then tracked across several consequent frames using optical flow. The motion of these points is analyzed and the face is classified as smiling or not smiling