Facial recognition has become one of the most widely used technologies utilized both in the civilian and government/military worlds. Facebook, airports, banks, and many other businesses incorporate this technology for added security, added features, and convenience measures. Even though Bledsoe, Chan, and Bisson began work on this in the 1960’s, many of the advancements began in the 1990s. Most algorithms are grouped into one of two categories, template-based or geometric-based. (Marques) Although this technology is rapidly expanding, it still has far many more possibilities such as 3-D facial recognition. The following paper will attempt to briefly highlight and discuss the main ideas of the most popular algorithms, as well as examine
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Facial recognition and detection algorithms work together to improve the accuracy of today’s facial recognition software. The job of today’s computer scientists and mathematicians is to mimic the human eyes and brain’s ability to detect and recognize human faces through an attempt to replicate this complicated process using a series of highly sophisticated algorithms. Infants learn these techniques shortly after birth, and today’s programmers are only beginning to scratch the surface of the possibilities created with this …show more content…
P_I= I ̂/I^* where I ̂ equals correctly identified and I* represents the size of the probe set.
The final comparisons are made by using all pairs for (PI,PJ) and for all of the ROC values which were measured by letting the ROC = R_k/P. As with the algorithms already discussed, the Linear Discriminant Analysis (LDA) model begins with Gaussian data. LDA makes use of two values, the mean and the variance, which are produced for each class. Classes can be thought of as facial features such as chins, noses, eyes, ears, hair lines, hair styles, etc. Four actual pieces of data are calculated during before the final algorithm can be used. The mean value is used to find the muk value.
Muk = 1/nk * sum(x). After this, the variance is found for all classes by using this mean value.
Sigma2 = 1 / (n-k) * sum((x-mu)2). There are two additional steps required before making a final prediction using the LDA method, this paper will only look at the final function.
Dk(x) = x * muk/〖sigma〗^2 -(〖muk〗^2/〖2sigma〗^2 +ln(Plk)) where “Dk(x) is the discriminate function for class k given input x, the muk, sigma2 and Plk are all estimated from your data. (Browniee). Once this discriminant is calculated, this data then can be further manipulated to assist in the facial recognition
Facial recognition and detection algorithms work together to improve the accuracy of today’s facial recognition software. The job of today’s computer scientists and mathematicians is to mimic the human eyes and brain’s ability to detect and recognize human faces through an attempt to replicate this complicated process using a series of highly sophisticated algorithms. Infants learn these techniques shortly after birth, and today’s programmers are only beginning to scratch the surface of the possibilities created with this …show more content…
P_I= I ̂/I^* where I ̂ equals correctly identified and I* represents the size of the probe set.
The final comparisons are made by using all pairs for (PI,PJ) and for all of the ROC values which were measured by letting the ROC = R_k/P. As with the algorithms already discussed, the Linear Discriminant Analysis (LDA) model begins with Gaussian data. LDA makes use of two values, the mean and the variance, which are produced for each class. Classes can be thought of as facial features such as chins, noses, eyes, ears, hair lines, hair styles, etc. Four actual pieces of data are calculated during before the final algorithm can be used. The mean value is used to find the muk value.
Muk = 1/nk * sum(x). After this, the variance is found for all classes by using this mean value.
Sigma2 = 1 / (n-k) * sum((x-mu)2). There are two additional steps required before making a final prediction using the LDA method, this paper will only look at the final function.
Dk(x) = x * muk/〖sigma〗^2 -(〖muk〗^2/〖2sigma〗^2 +ln(Plk)) where “Dk(x) is the discriminate function for class k given input x, the muk, sigma2 and Plk are all estimated from your data. (Browniee). Once this discriminant is calculated, this data then can be further manipulated to assist in the facial recognition