CNN for recognizing five different faces
<p>I have a project for face recognition of five people that I want my CNN to detect, and I was wondering if people could have a look at my model to see if this is a step in the right direction </p>

<pre><code>def model():

    model= Sequential()

    # sort out the input layer later
    model.add(convolutional.Convolution2D(64,3,3, activation='relu'), input_shape=(3,800,800))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(convolutional.Convolution2D(64,3,3, activation='relu'))
    model.add(convolutional.MaxPooling2D((2,2), strides=(2,2)))

    flatten()

    model.add(Dense(128, activation='relu'))
    model.add(Dropout(p=0.2))
    model.add(Dense(number_of_faces, activation='softmax'))
</code></pre>

<p>so the model will be taking in pictures (headshots found on google of 5 people) in 3 channels of size 800 by 800 with 64 feature maps, pooled and then another set of feature maps
and then connected to a mlp for classification into a binary vector for 5 output neurons.  My question is, is this a decent approach to try and classify headshots of certain people?
for example if I were to download one hundred pictures of a certain person and put them through this model, would the feature space created in the convolution be big enough to capture
the features of that face and four others?
thanks for the help guys </p>