Hi, I got a task for you to implement. Description: In this task , you will use the dataset Face.mat to classify
emotions, age, and gender of the subjects. This dataset includes 12 images of 6 different subjects. For
each subjects we have 2 samples of 6 different emotions. Use one of each samples in your training
dataset and the other one in your testing dataset. This way you would have 36 images in the training
dataset and 36 images in the testing dataset.
Goal: To classify the gender (2 classes: M, F), emotions (6 classes: angry, disgust, neutral, happy, sad,
surprised) and age (3 classes: Young, Mid age, Old). Using a linear classifier. You should label your
training and testing data for each of the classification problem separately.
Classifier: Use the linear classifier
Features: Projection of the image on each of the eigenfaces. For this purpose, you need to calculate
PCA of your training data. Your kth feature will be the projection (dot product) of the image on the
kth eigen vector (PCA direction) also known as eigenface.
Feature Selection: You will extract 36 Features as there are 36 images (and eigen vectors) in your
training data. Use the “sequentialfs” command in MATLAB to select the top 6 features using the
sequential forward search algorithm. The whos command on ‘Face.mat’ gives the following information about the dataset:
Variable Size Data Type Bytes
II 100820x72 double 58072320
m 1x72 double 576 . Make sure you understand the dataset details correctly. I can also help you with the few rows of II and m variables. II contains  -33.982443959531835	-44.41860741916287	-22.868746280499906	-25.379805594128143	-30.283148184883956	-30.058936718904974	-28.15310454274946	-26.01503669906765	-31.855455266812143	-33.236609799642935	-26.14460424518944	-26.285885736956956	-2.510206308272174	-8.53013291013687	-6.002529260067448	-8.408510216226944	2.1538781987700872	0.44219400912517415	-3.647589763935727	-8.353382265423534	-20.1724062685975	-9.886302320968056	-5.24480261852807	-16.29321563181908	-1.7801626661376702	-1.0608014282880447	11.498264233287045	2.2672783177940943	0.8750843086689173	2.5873834556635558	-8.029696488791913	-6.037314024995041	-2.089654830390799	7.025689347351715	6.521801229914701	-0.6049295774647874	0.6024102360642729	0.9641539377107762	-1.1782979567546192	-1.6539277921047386	-4.695278714540763	-3.6278218607419177	-1.7889506050386785	0.5992660186470999	-6.07371553263242	2.743602459829404	2.159244197579852	3.6410136877603634	10.375004959333467	1.2577068042055117	4.641668319777821	-0.08294981154533332	-6.972664153937714	-4.037085895655622	0.6801527474707427	2.5728030152747436	-1.4371751636580115	-1.4837234675659658	4.287442967665143	4.045744891886528	-3.421573100575287	-2.5918369371156587	-2.187462804999015	-0.8162269390993799	-2.0944852211862752	-6.876175362031347	-3.452370561396549	-5.16341995635787	2.52831779408848	-0.05173576671295166	-3.8864213449712395	-3.043354493156116
-33.982443959531835	-42.41860741916287	-20.868746280499906	-28.379805594128143	-31.283148184883956	-26.058936718904974	-29.15310454274946	-27.01503669906765	-27.855455266812143	-27.236609799642935	-29.14460424518944	-29.285885736956956	-4.510206308272174	-5.530132910136871	-3.0025292600674476	-10.408510216226944	0.15387819877008724	-4.557805990874826	-12.647589763935727	-2.3533822654235337	-17.1724062685975	-22.886302320968056	-0.24480261852806962	-16.29321563181908	-0.7801626661376702	6.939198571711955	10.498264233287045	4.267278317794094	6.875084308668917	5.587383455663556	-3.029696488791913	-6.037314024995041	4.910345169609201	7.025689347351715	2.5218012299147006	7.395070422535213	-2.397589763935727	2.964153937710776	-3.1782979567546192	0.3460722078952614	-6.695278714540763	-4.627821860741918	2.2110493949613215	-1.4007339813529	-4.07371553263242	0.743602459829404	1.1592441975798522	2.6410136877603634	14.375004959333467	2.2577068042055117	3.6416683197778212	-4.082949811545333	-4.972664153937714	-7.037085895655622	2.6801527474707427	-0.42719698472525636	0.5628248363419885	-3.483723467565966	1.287442967665143	2.045744891886528	-4.421573100575287	-4.591836937115659	-1.187462804999015	-1.81622693909938	-2.0944852211862752	-4.876175362031347	-7.452370561396549	-7.16341995635787	-3.47168220591152	-2.0517357667129517	-4.8864213449712395	-2.043354493156116
and m contains 113.98244395953184	119.41860741916287	112.8687462804999	109.37980559412814	123.28314818488396	124.05893671890497	118.15310454274946	116.01503669906765	106.85545526681214	116.23660979964293	113.14460424518944	112.28588573695696	91.51020630827217	95.53013291013687	104.00252926006745	104.40851021622694	109.84612180122991	109.55780599087483	98.64758976393573	100.35338226542353	100.1724062685975	100.88630232096806	104.24480261852807	103.29321563181908	105.78016266613767	106.06080142828804	102.50173576671295	106.7327216822059	110.12491569133108	107.41261654433644	109.02969648879191	112.03731402499504	107.0896548303908	106.97431065264828	107.4781987700853	106.60492957746479	108.39758976393573	106.03584606228922	111.17829795675462	110.65392779210474	113.69527871454076	112.62782186074192	109.78895060503868	111.4007339813529	111.07371553263242	110.2563975401706	108.84075580242015	111.35898631223964	93.62499504066653	99.74229319579449	99.35833168022218	103.08294981154533	104.97266415393771	104.03708589565562	99.31984725252926	98.42719698472526	102.43717516365801	101.48372346756597	97.71255703233486	97.95425510811347	98.42157310057529	97.59183693711566	100.18746280499902	101.81622693909938	100.09448522118628	99.87617536203135	99.45237056139655	99.16341995635787	99.47168220591152	98.05173576671295	96.88642134497124	99.04335449315612. The given sample data for variable II is of only two rows and the data given for variable 'm' is the complete data. Make sure understand everything correctly. Lets Start!