Yaş tahmin algoritması hiyerarşik yaklaşımı kullanır (şekil 10). İlk olarak, girdi parçaları üç yaş grubuna ayrılır: 18 yaşından küçük, 18-45 yaş arası ve 45 yaş üstü. İkinci olarak, bu adımın sonuçları, her biri tek bir on yıl ile sınırlı yedi küçük gruba bölünür. Böylelikle, çok sınıflı sınıflandırma problemi, bir dizi ikili “tümüne karşı bir” (one-against-all) sınıflandırıcılara (BC'ler) indirgenir. Sınıflandırıcı daha sonra görüntüleri ilişkili sınıfa göre sıralar ve nihai kararlara bu sıra dağılım grafikleri (histogramlar) analiz edilerek ulaşılır.Bu BC'ler iki seviyeli bir yaklaşım kullanılarak oluşturulmuştur. Öncelikle bir uyarlanabilir özellik uzayına, daha önce açıklandığı şekilde, dönüştürüldükten sonra, görüntüler RBF çekirdekli destek vektör makineleri kullanılarak sınıflandırılır.
Girdi parçaları, hizalanması ve tek tip bir ölçeğe dönüştürülmesi için parlaklık özelliklerine göre bir ön işleme tabi tutulur. Bu ön işleme adımı renk alanı dönüşümünü ve ölçeklendirmeyi içerir, her iki işlem de cinsiyet tanıma algoritmasında kullanılanlara benzer olan her iki işlem de cinsiyet tanıma algoritmasında kullanılanlara benzerdir. Özellikler, her renk bileşeni için hesaplanır ve tek tip bir özellik vektörü oluşturmak için birleştirilir.
Eğitim ve test, yeterince büyük bir renkli görüntü veri tabanı gerektirir. Son teknoloji MORPH ve FG-NET görüntü veri tabanlarını, farklı kaynaklardan elde edilmiş ve 10.500 yüz görüntüsünden oluşan kendi görüntü veri tabanımızla birleştirdik. Görüntülerdeki yüzler AdaBoost yüz algılama algoritmaları tarafından otomatik olarak tespit edildi.
Yaş sınıflandırma algoritmasının ilk aşamasını eğitmek ve test etmek için toplam 7000 görüntü kullanılmıştır. 144 uyarlanabilir özellik kullanılarak üç BC oluşturuldu.
Birinci aşama sınıflandırma sonuçları, genç yüzler için %82, orta yaşlı yüzler için %58 ve yaşlı yüzler için %92 doğruluk gösterdi. Üç yaş kategorisi için genel yaş sınıflandırması doğruluğu %77,3 idi.
İkinci aşama BC'ler, birinci aşama için olanlar ile aynı şekilde oluşturuldu (yukarıda açıklanmıştır). Şekil 11, önerilen algoritmanın ilk aşaması ile yaş tahmininin görsel bir örneğini göstermektedir.
The age
estimation algorithm realises hierarchical approach (fig. 10). First of all input
fragments are divided into1for three age groups: smaller than 18 years old,
from 18 - 45 years old and bigger than 45 years old. Afterwards the results of this
in the first stepage are more subdivided to seven smallernewer
groups, with
each limiteding
to one single decade. Thus the problem of multiclass classification is therefore
reduced to a set of binary ‘one-against-all’ classifiers (BC).These classifiers
calculate: ranks of each of the analyzed class. The total decision is obtained then
by the analysis the previously received rank histograms of ranks.
These BCs are
constructioned
using a two-level approach. After is appliedfirst with the 2transitioning to adaptive feature space,
asequal to this3
described earlier, and support vector machines classification of images with
RBF kernel.
Input fragments were
preprocessed for their luminance characteristics to align and to transform them
to uniformal scale. This pPreprocessing step includes
color-space transformation and scaling, both operations
similar to those used inthat of a gender
recognition algorithm. Features, calculated for each colour
component, are combined to form a uniform featured4
vector.
Training and testing5 require a sufficientlyhuge largeenough coloring
image database: We used state-of-the-art image databases MORPH and FG-NET with our
own image database, gathered from some6many sources, which comprised of 10,500 face images.
Faces on the images were detected automatically by AdaBoost face detection
algorithms.
A total number
of seven thousand images were used for age classification algorithm training
and testing on the first stage. 3 binary classifiers were made utilizing 144
adaptive features each of.
Classification
results on the first stage are: 82 % accuracy for young age, 58 % accuracy for
middle age and 92 % accuracy for senior age. Age classification accuracyrate
in a three age division problem – 77.3 %.
Binary classifiers
of the second stage were constructed equalsame to the first stage described above. A visual
example of age estimation by the proposed algorithm on its first stage is
presented in figs. 11.
The proposed age estimation algorithm realisesrealizes
hierarchical approach (figFig. 10). First of
all, the input fragments are divided into1for three
age groups: smallerless than 18 years old, from 18 - 45 years old and biggermore than 45 years old. Afterwards2Second,
the results of this in the firstfirst stepage are more further subdivided
tointo
seven smallernewer
groups, with
each limiteding
to onea
single decade. ThusThis reduces the problem
oforiginal multiclass
classification is therefore reduced problem
to a set of binary ‘”one-against-all’”3 classifiers (BC). These
classifiers calculate:Each classifier
then ranks of each of the analyzedthe images based on the associated class. The total decision is, and the final decisions are obtained then by the analysis
the previously received analyzing these rank histograms of ranks.
These BCs are
constructioned4
using a two-level approach. After is appliedfirst with the transitioning to an adaptive feature space, asequal to this5
described earlier, and the images are classified using support vector
machines classification of images with radial basis function (RBF) kernels.6
The Iinput fragments wereare preprocessed
forto align
and transform their luminance characteristics to align and to transform them to uniformala uniform scale. This
pPreprocessing step includes color-space transformation and
scaling, both operations similar to those used inthat of
athe 7gender
recognition algorithm. Features, are calculated for
each colourcolor
component, are and combined to form a uniform featured8 vector.
Training and testing9 require a sufficientlyhuge largeenough coloring
image database: We used. Here, we combined the state-of-the-art image databases MORPH and FG-NET image databases with our own image database,
gathered from some10many sources, which comprised of many sources and comprising 10,500 face images. Faces on. The faces
in the images were detected automatically by the AdaBoost face detection algorithms.
A total number of seven thousand7000 images were used for
to train and test the first stage of the
age classification algorithm training and testing
on the first stage. 3 binary classifiers.
Three BCs were made utilizingcreated, each with11 144 adaptive features each of.
Classification
results on the The first-stage are:classification results showed 82 % accuracy for
young agefaces,
58 % accuracy for middle age-aged faces, and 92 % accuracy for seniorelderly faces. The overall
age. Age classification accuracyrate
in afor the
three age division problem –categories was 77.3 %.12
Binary
classifiers of tThe
second-stage BCs
were constructed in the equalsame tomanner as the
first stage (described above. A). Fig. 11 shows a
visual example of age estimation by the first stage
of the proposed algorithm on its first
stage is presented in figs. 11..
The proposed age estimation algorithm realizes hierarchical approach (Fig. 10). First, the input fragments are divided into three age groups: less than 18 years old, 18–45 years old, and more than 45 years old. Second, the results of this first step are further subdivided into seven smaller groups, each limited to a single decade. This reduces the original multiclass classification problem to a set of binary “one-against-all” classifiers (BC). Each classifier then ranks the images based on the associated class, and the final decisions are obtained by analyzing these rank histograms.
These BCs are constructed using a two-level approach. After first transitioning to an adaptive feature space, as described earlier, the images are classified using support vector machines with radial basis function(RBF) kernels.
The input fragments are preprocessed to align and transform their luminance characteristics to a uniform scale. This preprocessing step includes color-space transformation and scaling, both operations similar to those used in the gender recognition algorithm. Features are calculated for each color component and combined to form a uniform feature vector.
Training and testing require a sufficiently large color image database. Here, we combined the state-of-the-art MORPH and FG-NET image databases with our own image database, gathered from many sources and comprising 10,500 face images. The faces in the images were detected automatically by the AdaBoost face detection algorithms.
A total of 7000 images were used to train and test the first stage of the age classification algorithm. Three BCs were created, each with 144 adaptive features.
The first-stage classification results showed 82% accuracy for young faces, 58% accuracy for middle-aged faces, and 92% accuracy for elderly faces. The overall age classification accuracy for the three age categories was 77.3%.
The second-stage BCs were constructed in the same manner as the first stage (described above). Fig. 11 shows a visual example of age estimation by the first stage of the proposed algorithm.
