Heterogeneous Parallel Ensemble Classifiers for Face Detection Abstract - Face detection is a process to determine the presence of a human face in a given image, and if exists then returns the location of detected face. Face detection is a difficult process due to certain factors, such as background complexity, illumination conditions, scale, expression, position, orientation and pose of the face. The presence of external facial features such as glasses, moustaches and beards further complicates the process of face detection. Face detection acts as a very first and an important step in applications such as face recognition, human computer interface, automatic surveillance and content-based image retrieval. In this paper, we proposed a face detection technique based on ensemble-classifiers, which employs multiple heterogeneous classifiers in parallel fashion. The classifiers may itself be ensembled in cascaded manner. The seminal work of Adaboost based face detection has shown robust classification capability while some of the non-face patterns are identified as face patterns. We will mainly focus on reducing the false positives produced by the Adaboost based face detection system by cascading Adaboost with (i) Neural Networks, (ii) Support Vector Machines and (iii) Bayesian Networks. Then all these three-cascaded configurations will be ensembled in a parallel fashion. We will be using eager learners (ANN, SVM, Bayesian networks etc) as they take more time for training but less time in prediction. Hence once trained eager learners predict quickly and are expected not to effect negatively on time efficiency of Adaboost algorithm. I. Introduction: Face identification is a two fold problem which involves face detection an... ... middle of paper ... ...y the weak classifier are assigned lower weights, whereas the examples that are misclassified are given higher weights in a moving probability function which defines the training samples. The random training becomes more deterministic, focusing on the examples that are difficult to classify. Adaboost algorithm for the two class problem is explained as follows. Given (xi; yi), … , (xm, ym) xi Є X; yi Є Y = {-1; +1} where X is the training set and Y is the label set. Weight distribution over X D1(i)=1/m Є (1) where i = 1; :::;m and D(i) is the set of weights over the training set. Initially all weights are set equally.The weight of this distribution on training example i on round t is denoted by Dt(i). _ For t = 1; :::; T, – Train a weak classifier h using a learning function L on samples X and weight distribution D ht(x) = L(X;Dt
The quantum system could also represent the energy level of the photon polarization direction; however, for the purpose of defining the bracket notation consider just the position of the particle.\footnote{This section focuses on the ``ket'' of the bracket notation. The matching ``bra'' $\bra{x}$ denotes the conjugate transpose of $\ket{x}$. This choice is arbitrary, but is the convention which is widely used when talking about quantum computing \cite{non}}\par
Also, the value of the variable may differ depending on the assignment statement. For example, if we have the assignment statement as,
I will take a 2x2 square on a 100 square grid and multiply the two
The following formula for expected value of sample information tutor which is used to compute expected value for discrete random variable shows given below.
Graham Vest stated, “the one absolutely unselfish friend a man may have in this selfish world….is the dog” at a Missouri trial in 1870, over 500 years after the first instalment of dogs in civil services (Orbaan, 15). Unlike man, canines possess a courageous attribute that does not fail even in the moment of an emergency and will not back down to daily challenges that may arise on the job each day. Law enforcement agencies around the world, both big and small, employ canines to assist officers in the field. Therefore, canine units have become an integral part of law enforcement around the world, yet to be able to detect bombs, drugs, or the like, canines and their handlers, must undergo extensive training to ensure “familiarization” between man and dog and its purpose.
In many fields, such as medical, credit scoring and quality control research, one can obtain binary data that can occasionally be misclassified. In an example of medical field, a healthy patient could be falsely diagnosed as having a disease or an unhealthy patient could be falsely diagnosed as not having a disease. Bross (1954) was first discovered the problem of ignoring the misclassification that could conclude an extremely biased in the results from the binary data. Tenenbein (1970) introduced two methods to correct the bias that occurs during the misclassification of the data. The first method that he suggested to collect the data from the training session by using double sample scheme; the second method is to gather the prior sufficient
Hirayama, T., Iwai, Y., & Yachida, M. (2007, May). Integration of facial position estimation and person identification for face authentication [Electronic Version]. Systems & Computers in Japan, 38(5), 43-58.
Equation 2: Bayes Theorem of Prior Probabilities A1.... ... middle of paper ... ... Cambridge: Cambridge University Press.
Batch mode operation and each batch are sequenced through a series of treatment stages (Arora, n.d).
Biometrics is a preset method to recognize a person based on a physiological or behavioral attribute. The present features are face recognition, fingerprints, handwriting, hand geometry, iris, vein, voice and retinal scan. In the early years of the 21st century, we find ourselves persistently moving further away from the stipulation of physical human interface playing a major part of basic everyday tasks. Striding ever closer to an programmed society, we interact more habitually with mechanical agents, unsigned users and the electronic information sources of the World Wide Web, than with our human counterpart. It is therefore possibly sardonic that identity has become such an important issue in the 21st century. Face recognition has been related as the divine Grail of biometric recognition systems, due to a number of noteworthy advantages over other methods of identification.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.
There is a wide spectrum of applications, from different security systems for crime prevention and investigation to commercial and private use. For example, doors that open automatically have existed for a long time. To save energy, if a smart camera is used instead of a simple motion detector, the camera can choose to open the door if a person is approaching or leave it closed if a person is just walking by the door. One of the most sophisticated tools for smart cameras is a method called facial recognition.