Gender Classification Model

Indeed, two people can never have the same fingerprints, it is unique in every human. But using fingerprints we can classify gender, whether it’s male or female. In this article, I will take you through a Gender Classification Model which I will train using Deep Learning and Convolutional Neural Networks.

Now you must be thinking about how I will do Gender Classification. I will build a Convolutional Neural Network that can classify the gender using the fingerprints. For this task, I will use a dataset that contains over 55000 images of fingerprints of each finger.

Also, Read – Work on Artificial Intelligence Projects.

Gender Classification using Fingerprints

I will first start with converting those images into pixels value to extract the features from the fingerprints. Then I will split the data into training, testing and validation sets. Now let’s start with importing the libraries we need for this task and performing some steps of data preparation:

import numpy as np import pandas as pd import seaborn as sns import tensorflow as tf import os import cv2 import matplotlib.pyplot as plt

Creating a Helper Function:

def extract_label(img_path,train = True): filename, _ = os.path.splitext(os.path.basename(img_path)) subject_id, etc = filename.split('__') if train: gender, lr, finger, _, _ = etc.split('_') else: gender, lr, finger, _ = etc.split('_') gender = 0 if gender == 'M' else 1 lr = 0 if lr == 'Left' else 1 if finger == 'thumb': finger = 0 elif finger == 'index': finger = 1 elif finger == 'middle': finger = 2 elif finger == 'ring': finger = 3 elif finger == 'little': finger = 4 return np.array([gender], dtype=np.uint16)

The above function will help us in extracting the features from the fingerprints. This function will work by iterating through the labels of the images that we will assign in the function. The function will return an array 0 and 1. The zeros will represent males and ones will be representing the females.

Now our next step is to load the image path to the function we created to iterate all over the images to find labels, you can download the dataset from here:

img_size = 96 def loading_data(path,train): print("loading data from: ",path) data = [] for img in os.listdir(path): try: img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) img_resize = cv2.resize(img_array, (img_size, img_size)) label = extract_label(os.path.join(path, img),train) data.append([label[0], img_resize ]) except Exception as e: pass data return data

Now, the next step is to assign various directories or folders to use the loading data function on all the images:

Real_path = "../input/aman/web/Real" Easy_path = "../input/aman/web/Altered-Easy" Medium_path = "../input/aman/web/Altered-Medium" Hard_path = "../input/aman/web/Altered-Hard" Easy_data = loading_data(Easy_path, train = True) Medium_data = loading_data(Medium_path, train = True) Hard_data = loading_data(Hard_path, train = True) test = loading_data(Real_path, train = False) data = np.concatenate([Easy_data, Medium_data, Hard_data], axis=0) del Easy_data, Medium_data, Hard_data

Now let’s randomize the data and test the arrays to see what our data looks like:

import random random.shuffle(data) random.shuffle(test)
array([[1,
        array([[160, 158, 158, ...,   0,   0,   0],
       [160, 105, 121, ...,   0,   0,   0],
       [160, 105, 255, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8)],
       [1,
        array([[160, 158, 158, ...,   0,   0,   0],
       [160, 105, 121, ...,   0,   0,   0],
       [160, 105, 255, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8)],
       [1,
        array([[160, 158, 158, ...,   0,   0,   0],
       [160, 105, 121, ...,   0,   0,   0],
       [160, 105, 255, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8)],
       ...,
       [0,
        array([[160, 158, 158, ...,   0,   0,   0],
       [160, 105, 121, ...,   0,   0,   0],
       [160, 105, 255, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8)],
       [0,
        array([[160, 158, 158, ...,   0,   0,   0],
       [160, 105, 121, ...,   0,   0,   0],
       [160, 105, 255, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8)],
       [1,
        array([[160, 158, 158, ...,   0,   0,   0],
       [160, 105, 121, ...,   0,   0,   0],
       [160, 105, 255, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8)]], dtype=object)

The output above is showing how our images are present in the data. The first array is representing the label value of 0 and then we have an array of pixel values. Don’t get confused, the above array is the real representation of how the computer sees an image. For example:

Image for post

Now I will split the image arrays and image labels:

img, labels = [], [] for label, feature in data: labels.append(label) img.append(feature) train_data = np.array(img).reshape(-1, img_size, img_size, 1) train_data = train_data / 255.0

Building CNN for Gender Classification Model

Now, here I will build a Convolutional Neural Network for Gender classification model:

from tensorflow.keras import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten from tensorflow.keras import layers from tensorflow.keras import optimizers model = Sequential([ Conv2D(32, 3, padding='same', activation='relu',kernel_initializer='he_uniform', input_shape = [96, 96, 1]), MaxPooling2D(2), Conv2D(32, 3, padding='same', kernel_initializer='he_uniform', activation='relu'), MaxPooling2D(2), Flatten(), Dense(128, kernel_initializer='he_uniform',activation = 'relu'), Dense(2, activation = 'softmax'), ])

Now, before moving forward, let’s have a quick look at the summary of our CNN Model:

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 96, 96, 32)        320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 48, 48, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 48, 48, 32)        9248      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 24, 24, 32)        0         
_________________________________________________________________
flatten (Flatten)            (None, 18432)             0         
_________________________________________________________________
dense (Dense)                (None, 128)               2359424   
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 258       
=================================================================
Total params: 2,369,250
Trainable params: 2,369,250
Non-trainable params: 0

Now, I will compile the model using Adam optimizers, with a learning rate of 10%, and to prevent our CNN model from overfitting, I will be using the early_stopping_call method:

model.compile(optimizer = optimizers.Adam(1e-3), loss = 'categorical_crossentropy', metrics = ['accuracy']) early_stopping_cb = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

Now, let’s fit our Gender Classification Model, We are going to train the model for 30 epochs.

history = model.fit(train_data, train_labels, batch_size = 128, epochs = 30, validation_split = 0.2, callbacks = [early_stopping_cb], verbose = 1)

You will see a very long output for this of 30 epochs. It will use GPU, So I will recommend you to use Google colab for better performance. It will give train loss and accuracy and also validation loss and accuracy per epochs.

Now, we have successfully trained our CNN for Gender Classification Model with a training accuracy of 99 per cent and Validation accuracy of 98 per cent. Now, let’s have a look at the performance of our Gender Classification Model:

import pandas as pd import matplotlib.pyplot as plt pd.DataFrame(history.history).plot(figsize = (8,5)) plt.grid(True) plt.gca().set_ylim(0,1)
Gender Classification

Testing Gender Classification Model

As we split the training images into image labels and image arrays while training our model, we need to repeat the same process on the test images:

test_images, test_labels = [], [] for label, feature in test: test_images.append(feature) test_labels.append(label) test_images = np.array(test_images).reshape(-1, img_size, img_size, 1) test_images = test_images / 255.0 del test test_labels = to_categorical(test_labels, num_classes = 2)

Now, let’s evaluate the performance of our CNN model on the test set:

model.evaluate(test_images, test_labels)
6000/6000 [==============================] - 1s 134us/sample - loss: 0.0231 - accuracy: 0.9972 [0.023149466800407026, 0.9971667]

So, we got an accuracy of 99.72 per cent and a loss value of 0.0126 on our gender classification model. I hope you liked this article, feel free to ask your valuable questions in the comments section below. Don’t forget to subscribe for my daily newsletters if you like my work.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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