The person appears to be a/an Indian Female, approximately 20 to 29 years old.
Here’s a simple Keras model you can use for your deep learning project:

```python
import tensorflow as tf

model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(5,)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(5, activation='linear')
])

model.compile(loss='mse', optimizer='Adam')
```

This model is structured to take in 5 inputs and produce 5 outputs, which is great for various regression tasks in deep learning.
Here’s a simple Keras model that you can use: 

```python
import tensorflow as tf

model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(5,)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(5, activation='linear')
])

model.compile(loss='mse', optimizer='Adam')
```

This model consists of an input layer that takes 5 features, followed by four hidden layers with 128 neurons each, and an output layer that produces 5 outputs. The 'relu' activation function is commonly used for hidden layers, while 'linear' is suitable for the output layer when predicting continuous values.